Real property monitoring systems and methods for detecting damage and other conditions

ABSTRACT

Machine learning systems, methods, and techniques for detecting damage and/or other conditions associated with a building, land, structure, or other real property using a real property monitoring system are disclosed. The property monitoring system is used in conjunction with machine learning techniques to determine and/or predict various conditions associated with the real property, including particular damage thereto, e.g., based upon dynamic characteristic data obtained via on-site sensors, static characteristic data, third-party input descriptive of an event impacting the building, etc. Accordingly, damage and/or loss associated with the building/real property is more quickly and/or accurately ascertained so that suitable mitigation techniques may be applied. In some scenarios, previously undetectable or uncharacterized damage and/or other conditions may be discovered and mitigated.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is continuation of, and claims the benefit of, U.S.patent application Ser. No. 16/136,501, filed Sep. 20, 2018 and entitled“Real Property Monitoring Systems and Methods for Detecting Damage andOther Conditions,” which claims priority to and the benefit of:

-   -   U.S. Prov. App. 62/564,055 filed Sep. 27, 2017 and entitled        “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING        DAMAGE AND OTHER CONDITIONS;”    -   U.S. Prov. App. 62/580,655 filed Nov. 2, 2017 and entitled        “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE        AND OTHER CONDITIONS;”    -   U.S. Prov. App. 62/610,599 filed Dec. 27, 2017 and entitled        “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE        AND OTHER CONDITIONS;”    -   U.S. Prov. App. 62/621,218 filed Jan. 24, 2018 and entitled        “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS MITIGATION        AND CLAIMS HANDLING;”    -   U.S. Prov. App. 62/621,797 filed Jan. 25, 2018 and entitled        “AUTOMOBILE MONITORING SYSTEMS AND METHODS FOR LOSS RESERVING        AND FINANCIAL REPORTING;”    -   U.S. Prov. App. 62/580,713 filed Nov. 2, 2017 and entitled “REAL        PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING DAMAGE AND        OTHER CONDITIONS;”    -   U.S. Prov. App. 62/618,192 filed Jan. 17, 2018 and entitled        “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR DETECTING        DAMAGE AND OTHER CONDITIONS;”    -   U.S. Prov. App. 62/625,140 filed Feb. 1, 2018 and entitled        “SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR        BUILDING/REAL PROPERTY INSURANCE;”    -   U.S. Prov. App. 62/646,729 filed Mar. 22, 2018 and entitled        “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR LOSS        MITIGATION AND CLAIMS HANDLING;”    -   U.S. Prov. App. 62/646,735 filed Mar. 22, 2018 and entitled        “REAL PROPERTY MONITORING SYSTEMS AND METHODS FOR RISK        DETERMINATION;”    -   U.S. Prov. App. 62/646,740 filed Mar. 22, 2018 and entitled        “SYSTEMS AND METHODS FOR ESTABLISHING LOSS RESERVES FOR        BUILDING/REAL PROPERTY INSURANCE;”    -   U.S. Prov. App. 62/617,851 filed Jan. 16, 2018 and entitled        “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE        PRICING AND UNDERWRITING;”    -   U.S. Prov. App. 62/622,542 filed Jan. 26, 2018 and entitled        “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE        LOSS MITIGATION AND CLAIMS HANDLING;”    -   U.S. Prov. App. 62/632,884 filed Feb. 20, 2018 and entitled        “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE        LOSS RESERVING AND FINANCIAL REPORTING,” and    -   U.S. Prov. App. 62/652,121 filed Apr. 3, 2018 and entitled        “IMPLEMENTING MACHINE LEARNING FOR LIFE AND HEALTH INSURANCE        CLAIMS HANDLING,”        the entire disclosures of which are hereby incorporated by        reference herein in their entireties.

FIELD OF INVENTION

Machine learning methods facilitate loss mitigation and prevention, andthe automation of the insurance claims handling process, as well as thecustomer experience.

BACKGROUND

As computer and computer networking technology has become less expensiveand more widespread, more and more devices have started to incorporatedigital “smart” functionalities. For example, controls and sensorscapable of interfacing with a network may now be incorporated intodevices such as appliances, security systems, light switches, and watervalves, and other portions of building monitoring systems. Furthermore,it is possible for one or more central controllers to interface with thesmart devices to facilitate monitoring, automation, and securityapplications for a building.

However, such systems may not be able to automatically detect andcharacterize various conditions associated with a building. For example,when sensors detect water in a basement of a building, such systems maynot be able to automatically determine whether the water in the basementis due to an outside water main breaking and flooding the property, orwhether a levee has been breached and the entire neighborhood isflooded. In another example, such monitoring systems may not be able todetect or sufficiently identify and describe damage that is hidden fromhuman view, and that typically has to be characterized by explicit humanphysical exploration, such as damage between walls or in foundations,extent and range of electrical malfunctions, etc. Conventional systemsfurther may not be able to formulate precise characterizations of losswithout including unconscious biases, and may not be able to equallyweight all historical data in determining loss mitigation factors.

SUMMARY

The present disclosure generally relates to systems and methods fordetecting damage, loss, and/or other conditions associated with abuilding, land, structure, or other real property using a propertymonitoring system. The property monitoring system may be used inconjunction with machine learning techniques to facilitate lossmitigation and prevention, and the handling of an insurance claim, andenhancing the customer experience. Embodiments of exemplary systems andcomputer-implemented methods are summarized below. The methods andsystems summarized below may include additional, less, or alternatecomponents, functionality, and/or actions, including those discussedelsewhere herein.

In one aspect, a real property monitoring system may include a pluralityof sensors fixedly disposed at respective locations at a building. Eachsensor may monitor a respective dynamic, physical characteristicassociated with the building, and at least some of the plurality ofsensors may be fixedly attached to the building. The real propertymonitoring system may also include one or more user interfaces via whichthe real property monitoring system and end-users (e.g., residents,tenants, property owners, property managers, etc.) of the real propertymonitoring system communicate; one or more processors; and a datastorage entity communicatively connected to the one or more processors,and storing dynamic characteristic data that is indicative of respectivedynamic, physical characteristics detected by the plurality of sensors.The dynamic characteristic data may be generated based upon signalstransmitted by the plurality of sensors, for example. Additionally, thereal property monitoring system may include one or more networkinterfaces via which third-party input is received at the real propertymonitoring system. The third-party input may include digitizedinformation that is descriptive of an event impacting the building, suchas digital text, notes, images, etc. Typically, the third-party that orwho has generated the contents of the third-party input is not anend-user of the real-property monitoring system.

Further, the real property monitoring system may include a damagedetection module including a set of computer-executable instructionsstored on one or more memories. The set of computer-executableinstructions, when executed by the one or more processors, may cause thesystem to train, by utilizing the third-party input and the dynamiccharacteristic data corresponding to the building, an analytics modelthat is predictive of one or more conditions associated with thebuilding. The system may apply the trained-analytics model to at leastone of the dynamic characteristic data corresponding to the building oradditional characteristic data corresponding to the building to therebydiscover or predict at least one of the one or more conditionsassociated with the building. The one or more discovered conditions mayinclude particular damage to the building that is associated with theevent, e.g., particular damage to the building that is caused at leastin part by the occurrence of the event, and optionally other conditions.An indication of the particular damage to the building (and any otherdiscovered conditions corresponding to the building) may be transmittedby the real property monitoring system to at least one of a remotecomputing device or a user interface.

In another aspect, a computer-implemented method of detecting damage andother conditions at a building may include monitoring, using a pluralityof sensors included in a real property monitoring system, a plurality ofdynamic, physical characteristics associated with the building. Theplurality of sensors may be fixedly disposed at respective locations atthe building, and at least some of the plurality of sensors may befixedly attached to the building. The method may include storing dynamiccharacteristic data that is indicative of the plurality of dynamic,physical characteristics associated with the building and monitored bythe plurality of sensors. The dynamic characteristic data may begenerated based upon signals transmitted by the plurality of sensors,and stored in a data storage entity included in the real propertymonitoring system, for example. Additionally, the method may includeobtaining input whose content is generated by a third-party. Thethird-party input may include digitized or digital data that isdescriptive of an event impacting the building, and may include note,text, images, and other types of digital data, and the third-party inputmay be obtained via a network interface of the real property monitoringsystem that is different than, or excluded from, a set of userinterfaces via which end-users of the real property monitoring system(e.g., residents, tenants, property owners, property managers, etc.)communicate with the real-property monitoring system. Typically, thethird-party that or who generates the content included in third-partyinput is not an end-user of the real-property monitoring system.

The computer-implemented method may further include training, by usingthe third-party input, the dynamic characteristic data of the building,and optionally other data, an analytics model that is predictive of oneor more conditions associated with the building. The training may beperformed, for example, by an information processor included in the realproperty monitoring system. The method may also include applying, e.g.,by the information processor, the trained, analytics model to at leastone of the dynamic characteristic data corresponding to the building oradditional characteristic data corresponding to the building, therebydiscovering or predicting at least one of the one or more conditionsassociated with the building, one of which may be particular damage tothe building that is associated with the event. For instance, theoccurrence of the event may have at least in part caused the particulardamage to the building that has been discovered via the use of thetrained analytics model. Other conditions associated with the buildingwhich may be discovered include, for example, a cause of losscorresponding to the event and/or to the particular damage, anadjustment to one or more terms of an insurance policy providinginsurance coverage for the building, an adjustment to the pricing of agroup of insurance policies, one of which provides insurance coveragefor the building, and the like. The method may further includetransmitting an indication of the particular damage to the buildingand/or or other discovered conditions to at least one of a remotecomputing device or a user interface.

In yet another aspect, a computer-implemented method of detecting and/orestimating damage may include receiving, e.g., via one or moreprocessors and/or associated transceivers (such as via wiredcommunication or data transmission, and/or wireless communication ordata transmission over one or more radio links or communicationchannels), free form text or voice/speech associated with a submittedinsurance claim for a damaged insured asset, where the damaged insuredasset comprises a building. The method may also include identifying,e.g., via one or more processors, one or more key words within the freeform text or voice/speech; and/or based upon the one or more keywords,determining, e.g., via one or more processors, a cause of loss and/orperil that caused damage to the damaged insured asset to facilitatehandling an insurance claim, loss mitigation, and enhancing onlinecustomer experience.

In still another aspect, a computer-implemented method of determiningdamage to property may include inputting, e.g., via one or moreprocessors, historical claim data into a machine learning algorithm totrain the algorithm to identify one or more insured assets, respectivetypes of the one or more insured assets, respective insured assetfeatures or characteristics, one or more perils associated with the oneor more insured assets, and/or respective repair or replacement costs ofat least a portion of the one or more insured assets, wherein the one ormore insured assets comprise a building or type of real property, suchas a house or a home. The method may further include receiving, e.g.,via the one or more processors and/or one or more transceivers (such asvia wireless communication or data transmission over one or more radiolinks or communication channels), one or more images, such as digitalimages, of a damaged insured asset (such as digital or electronic imagessubmitted by the insured via a webpage, website, and/or mobile device);and/or inputting, via one or more processors, the images of the damagedinsured asset into a processor having the trained machine learningalgorithm installed in a memory unit, where the trained machine learningalgorithm identifies, based upon the input image(s), a type of thedamaged insured asset, one or more features or characteristics of thedamaged insured asset, a peril associated with the damaged insuredasset, and/or a repair or replacement cost of at least a portion of thedamaged insured asset to facilitate handling an insurance claimassociated with the damaged insured asset, and damage mitigation andenhancing the customer experience.

In another aspect, a computer system configured to detect and/orestimate damage may include one or more processors, sensors,transceivers, and/or servers configured to receive (such as via wiredcommunication or data transmission, and/or wireless communication ordata transmission over one or more radio links or communicationchannels) free form text associated with a submitted insurance claim fora damaged insured asset, where the damaged insured asset comprises abuilding or another type of real property. The one or more processors,sensors, transceivers, and/or servers may be further configured toidentify one or more key words included in the free form text; and/orbased upon the one or more keywords, determine a cause of loss and/orperil that caused damage to the damaged insured asset to facilitatehandling an insurance claim, mitigating damage, and enhancing onlinecustomer experience.

In yet another aspect, a computer system configured to determine damageto real property comprises one or more processors, servers, sensors,and/or transceivers configured to input historical claim data into amachine learning algorithm to train the algorithm to identify an asset(or type thereof), at least one feature or characteristic of the asset,a peril, and/or a repair or replacement cost of at least a portion ofthe asset, where the asset comprises real property. Additionally, theone or more processors, servers, sensors, and/or transceivers may befurther configured to receive (such as via wired communication, and/orvia wireless communication or data transmission over one or more radiolinks or communication channels), one or more images, such as digitalimages, of a damaged insured asset (such as one or more images submittedby the insured via a webpage, website, or mobile device); and/or inputthe one or more images of the damaged insured asset into a processorhaving the trained machine learning algorithm installed in a memoryunit, where the trained machine learning algorithm identifies, e.g.,based upon the one or more images, a type of the damaged insured asset,one or more features or characteristics of the damaged insured asset, aperil associated with the damaged insured asset, and/or a repair orreplacement cost of at least a portion of the damaged insured asset tofacilitate handling an insurance claim associated with the damagedinsured asset, mitigating damage, and enhancing the customer experience.

In another aspect, a computer system configured to determine damage toreal property comprises one or more processors, servers, sensors, and/ortransceivers configured to input historical claim data into a machinelearning algorithm to train the algorithm to develop a risk profile foran insurable asset based upon a type of the insurable asset and at leastone feature or characteristic of the insurable asset, where theinsurable asset comprises real property. The one or more processors,servers, sensors, and/or transceivers may also be configured to receive(such as via wired communication or data transmission, and/or wirelesscommunication or data transmission over one or more radio links orcommunication channels), one or more images, such as digital imageacquired via a mobile device or smart home controller, of an undamagedinsurable asset (such as one or more images submitted by an insuredparty via a webpage, website, and/or mobile device); and/or input theone or more images of the undamaged insurable asset into a processorhaving the trained machine learning algorithm installed in a memoryunit. Based upon the one or more images, the trained machine learningalgorithm may identify or determine a risk profile for the undamagedinsurable asset to facilitate generating an insurance quote for theundamaged insurable asset, mitigating and preventing damage, andenhancing the customer experience.

In still another aspect, a computer-implemented method for determiningdamage to real property may comprise, e.g., via one or more processors,servers, sensors, and/or transceivers, inputting, via the one or moreprocessors, historical claim data into a machine learning algorithm totrain the algorithm to develop respective risk profiles for at least oneinsurable asset based upon a type of the at least one insurable assetand at least one feature or characteristic of the at least one insurableasset. The at least one insurable asset may comprise real property suchas a building, house, or home. The method may also include receiving,e.g., via the one or more processors and/or transceivers (such as viawired communication or data transmission, and/or via wirelesscommunication or data transmission over one or more radio links orcommunication channels) one or more images, such as digital imageacquired via a mobile device or smart home controller, of an undamagedinsurable asset (such as one or more images submitted by an insuredparty via a webpage, website, and/or mobile device); and/or inputting,e.g., via the one or more processors, the one or more images of theundamaged insurable asset into a processor having the trained machinelearning algorithm installed in a memory unit, where the trained machinelearning algorithm, identifies or determines a risk profile for theundamaged insurable asset based upon the one or more images tofacilitate generating an insurance quote for the undamaged insurableasset, damage mitigation and prevention, and enhancing the customerexperience.

Advantages will become more apparent to those skilled in the art fromthe following description of the preferred embodiments which have beenshown and described by way of illustration. As will be realized, thepresent embodiments may be capable of other and different embodiments,and their details are capable of modification in various respects.Accordingly, the drawings and description are to be regarded asillustrative in nature and not as restrictive.

BRIEF DESCRIPTION OF THE DRAWINGS

The Figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each Figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the Figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingFigures, in which features depicted in multiple Figures are designatedwith consistent reference numerals.

There are shown in the drawings arrangements which are presentlydiscussed, it being understood, however, that the present embodimentsare not limited to the precise arrangements and instrumentalities shown,wherein:

FIG. 1 illustrates a block diagram of an exemplary real propertymonitoring system for detecting damage and/or loss associated with abuilding, structure, land, and/or other real property that may operatein accordance with the described embodiments;

FIG. 2 illustrates a block diagram of an exemplary real propertymonitoring system controller which may be included in the system of FIG.1;

FIG. 3 illustrates a flow diagram of an exemplary computer-implementedmethod for detecting damage using a real property monitoring system thatmay operate in accordance with the described embodiments;

FIG. 4 depicts an exemplary computing environment in which techniquesfor training a neural network to identify a risk level of a building orother real property may be implemented, according to one embodiment;

FIG. 5 depicts an exemplary computing environment in which techniquesfor collecting and processing user input, and training a neural networkto identify a risk level of a real property may be implemented,according to one embodiment;

FIG. 6 depicts an exemplary artificial neural network which may betrained by the neural network unit of FIG. 4 or the neural networktraining application of FIG. 5, according to one embodiment andscenario;

FIG. 7 depicts an exemplary neuron, which may be included in theartificial neural network of FIG. 6, according to one embodiment andscenario;

FIG. 8 depicts text-based content of an exemplary electronic claimrecord that may be processed by an artificial neural network, in oneembodiment;

FIG. 9 depicts a flow diagram of an exemplary computer-implementedmethod of determining a risk level posed by a particular real property,according to one embodiment;

FIG. 10 depicts a flow diagram of an exemplary computer-implementedmethod of identifying risk indicators from real property information,according to one embodiment;

FIG. 11 depicts a flow diagram of an example computer-implemented methodof detecting and/or estimating damage to real property, according to oneembodiment;

FIG. 12 illustrates a flow diagram of an exemplary computer-implementedmethod of determining damage to property that may operate in accordancewith the described embodiments;

FIG. 13 illustrates a block diagram of an exemplary computer systemconfigured to detect and/or estimate damage to real property, where thecomputer system may be included in the system of FIG. 1; and

FIG. 14 illustrates a block diagram of an exemplary computer systemconfigured to determine damage to real property, where the computersystem may be included in the system of FIG. 1.

The Figures depict preferred embodiments for purposes of illustrationonly. One skilled in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof the invention described herein.

DETAILED DESCRIPTION

Artificial Intelligence System for Homeowners Insurance

The present embodiments are directed to, inter alia, machine learningand/or training a model using historical home/property insurance claimdata to discover risk levels and price home/real property insuranceaccordingly. Systems and methods may include natural language processingof free-form notes/text, or free-form speech/audio, recorded by callcenter and/or claim adjustor, photos, and/or other evidence. Thefree-form text and/or free-form speech may also be received from acustomer who is inputting the text or speech into a mobile device app orinto a smart home controller, and/or into a chat bot or robo-advisor.

Other inputs to a machine learning/training model may be harvested fromhistorical claims may, and may include make, model, year of appliancesin the house (e.g., water heater, toilet, dishwasher, etc.), type ofhome, materials used in building the home, claim paid or not paid,liability (e.g., types of injuries, where treated, how treated, etc.),disbursements related to claim such as hotel costs and other payouts,etc. Additional inputs to the machine learning/training model mayinclude home telematics data received from a smart home controller, suchas how long and when are the doors unlocked, how often is the securitysystem armed, how long is the stove on and during which times of theday, etc.

The present embodiments may facilitate discovering new causes of lossthat may be utilized to set pricing of insurance. Causes of loss forhomeowners may include wind, hail, fire, mold, etc. The presentembodiments may dynamically characterize insurance claims, and/ordynamically determine causes of loss associated with insurance claims,which may vary geographically. The present embodiments may dynamicallyupdate pricing models to facilitate better matching insurance premiumprice to actual risk.

Exemplary Real Property Monitoring System for Detecting Damage

FIG. 1 illustrates a block diagram of an exemplary real propertymonitoring system 100. The high-level architecture includes bothhardware and software applications, as well as various datacommunications channels for communicating data between the varioushardware and software components. Generally, the real propertymonitoring system 100 may automatically monitor conditions and/orcharacteristics (which may be dynamically occurring) of a building,structure, land, and/or other type of real property, e.g., anydesignated portion of land and/or anything permanently placed on orunder the designated portion of land.

The real property monitoring system 100 may be roughly divided intofront-end components 102 and back-end components 104. The front-endcomponents 102 may be disposed within, on, or at a physical realproperty, such as within, on, or at a residential or non-residentialbuilding 130. For example, the exemplary real property monitoring system100 may be installed in, or at, a single-family house, an apartmentbuilding, or a condominium, or even in or at a non-residential location,such as business, warehouse, school, government building, museum, etc.For ease of reading and illustration herein, the system 100 is describedas monitoring a building 130, however, it is understood that the system100 and/or any of the techniques, methods, apparatuses, and/or devicesdescribed herein may be easily applied to other types of real property.

Further, while some of the exemplary front-end components 102 aredescribed as being disposed within or inside the building 130, it isunderstood that some or all of the front-end components 102 may beinstalled outside of or nearby the building 130. For example, one ormore front-end components 102 may be fixedly attached to the interiorand/or the exterior of the building 130, and/or fixedly attached torespective supports or fixtures that are located on the particularportion of land or real estate on which the building 130 is situated.Additionally or alternatively, one or more front-end components 102 maybe removably attached to the interior and/or the exterior of thebuilding 130, and/or removably attached to respective supports orfixtures that are located on the particular portion of land or realestate on which the building 130 is situated.

Generally speaking, as used herein, one or more front-end components 102that are installed “at” a building 130 may be disposed inside, outside,around, and/or nearby the building 130. Further still, in oneembodiment, one or more of the front-end components 102 may be disposedat a location that is remote from the building 130. For example, theremote intelligent monitoring system controller 106R may be locatedremotely from the building 130 and communicatively connected with otherfront-end components 102, e.g., via the network 132. Generally, though,the front-end components 102 are positioned and/or located so that thesystem 100 is able to monitor conditions at the building 130.

The real property monitoring system 100 may include an intelligentmonitoring system controller 106, one or more control devices 110, oneor more sensors 112, one or more appliances 114, one or more displays116, and/or one or more user input devices or user interfaces 118, whichare collectively referred to herein as “intelligent building products.”Typically, but not necessarily, the real property monitoring system 100may include multiples of the intelligent building products 110, 112,114, 116, and/or 118. For example, the real property monitoring system100 may include a plurality of control devices 110, a plurality ofsensors 112, a plurality appliances 114, a plurality of displays 116,and/or a plurality of user interfaces 118. In some arrangements (notshown), the front-end components 102 may also include a back-up powersupply (e.g., battery, uninterruptable power supply, generator, etc.).

The front-end components 102 may be connected to each other via one ormore links 120 and/or may be connected to a monitoring system network108 by the link(s) 120. The one or more links 120 may include at leastone of a wired connection, a wireless connection (e.g., one of the IEEE802.11 standards), an optical connection, etc. In certain embodiments inwhich the real property monitoring system 100 may include a remoteintelligent monitoring system controller 106R, the remote intelligentmonitoring system controller 106R may be communicatively connected tothe monitoring system network 108 via another network 132 and the dataand/or communication links 122, 128, as is described in more detail in alater section below.

Exemplary Block Diagram of Real Property Monitoring System

FIG. 2 illustrates a more detailed block diagram of the exemplaryintelligent monitoring system controller 106 of FIG. 1. The intelligentmonitoring system controller 106 may include a controller 202 that isoperatively connected to a database 210 via a link 218. It should benoted that, while not shown, additional databases may be linked to thecontroller 202 in a known manner. Additionally, the controller 202 mayinclude a program memory 204, a processor 206 (may be called amicrocontroller or a microprocessor), a random-access memory (RAM) 208,and an input/output (I/O) circuit 214, all of which may beinterconnected via an address/data bus 216. It should be appreciatedthat although only one microprocessor 206 is shown, the controller 202may include multiple microprocessors 206. Similarly, the memory of thecontroller 202 may include multiple RAMs 208 and multiple programmemories 204. Further, although the I/O circuit 214 is shown as a singleblock, it should be appreciated that the I/O circuit 214 may include anumber of different types of I/O circuits. The program memory 204 and/orthe RAM 208 may include or store a graphical user interface 220 and anintelligent monitoring system application 222, for example.

The graphical user interface 220 may include a set of computer-readableor computer-executable instructions that, when executed by the processor206, cause the display(s) 116/116R and the user input device(s) or userinterface(s) 118/118R to display information, e.g., to an end-user,and/or to receive input from the end-user. As used herein, the term“end-user” refers to a user or operator of the real property monitoringsystem 100 who uses the building 130 and/or is responsible, at least inpart, for the condition and/or safety of and associated with thebuilding 130. There may be more than one user or operator of the realproperty monitoring system 100 (e.g., a family, a staff of people,etc.). Further, the set of end-users of the real property monitoringsystem 100 associated with the building 130 may include a primary user(e.g., the owner of the building 130, a tenant of the building 130, aproperty manager of the building 130, or the person under whose name themonitoring account is held for the building 130) and one or moreauthorized secondary users (e.g., a personal assistant of the primaryuser, a dependent child of the primary user, employees of a tenant thebuilding, etc.).

End-users may communicate with the real property monitoring system 100via a local user interface that is disposed at the building 130 (e.g.,devices 116, 118). For example, the local user interfaces 116, 118 mayinclude panels, touchscreens, etc. that are fixedly attached at variouslocations inside of the building and/or at various proximate locationsexternal to the building, such as on the parcel of land or real estateon which the building is located. Additionally or alternatively,end-users may communicate with the real property monitoring system 100via a remote user interface (e.g., devices 116R, 118R), such as a mobileor smart device, laptop, tablet, or the like, which may be physicallydisposed (e.g., when ported by the end-user) inside the building or atsome other remote location.

It is noted that, in some implementations, a local display 116 and alocal user interface 118 may be an integral device, and/or a remotedisplay 116R and a remote user input device 118R may be another integraldevice. For example, the monitoring system 100 may include one or moreintelligent building control panels that are fixedly disposed within orat the building 130, such as a downstairs building control panel and anupstairs intelligent building control panel, and/or may include one ormore control panels that are implemented on one or more mobile devicesvia which end-users may utilize to communicate with the system 100.

Such local and/or remote control panels may respectively include, forexample, a display and/or input product (e.g., a touchscreen) and mayperform the functions of an intelligent monitoring system controller 106as described above. For example, such an intelligent building controlpanels may be used to receive user input to the real property monitoringsystem 100 as described above, and/or to display statuses, alerts,and/or alarms to the end-user.

The intelligent monitoring system application 222 may include a set ofcomputer-executable or computer-readable instructions that, whenexecuted by the processor 206, cause the intelligent monitoring systemcontroller 106 to carry out one or more of the functions associated withthe real property monitoring system 100 described herein. Variousfunctions of the real property monitoring system 100 may be implementedby one or more respective operating modules included in the intelligentmonitoring system application 222, which may be implemented as one ormore software applications and/or one or more software routines (e.g.,computer-executable instructions that are stored on the memory 204 andthat are executable by the processor 206). For example, a monitoringmodule or local monitor 224 may implement functionality for monitoringone or more dynamic, physical characteristics and/or conditions of thebuilding 130, and a damage detection module or damage detector 226 mayimplement functionality for determining and/or detecting damage, loss,and/or other conditions associated with the building 130. More detaileddescriptions of the local monitor 224 and of the damage detector 226 areprovided in other sections of this disclosure.

Of course, the intelligent monitoring system application 222 may not belimited to including only the local monitor 224 and the damage detector226, and may include one or more other modules 228 to implement desiredfunctionality. Similarly, the program memory 204 may store one or moreapplications 230 other than the graphical user interface 220 and theintelligent monitoring system application 222 as desired. Further, insome implementations, some or all of the operating modules,applications, or portions thereof may be stored in the back-endcomponents 104 and implemented by the back-end components 104, or onanother instance of another controller 106 associated with the building130, which may be the remote controller 106R or another controller (notshown). In a non-limiting example, the monitoring module 224 may beincluded in the controller 106 at the building 130, while the damagedetection module 226 may be included in the controller 106R that isremotely situated from the building 130.

The RAM(s) 208 and program memories 204 of the controller 202 may beimplemented as one or more non-transitory, tangible computer-storagemedia, such as one or more semiconductor memories, magnetically readablememories, biological memories, and/or optically readable memories, forexample. The controller 202 may further include and/or maycommunicatively connect (e.g., via the link 218) to one or moredatabases 210 or other data storage mechanisms or entities 210 (e.g.,one or more hard disk drives, optical storage drives, solid statestorage devices, etc.), which may include one or more respectivenon-transitory, tangible computer-storage media.

In one embodiment, at least one of the data storage entities 210 islocal to the controller 202 and, in some implementations, may beincluded with the controller 202 in an integral device. In anotherembodiment, at least some of the data storage entities 210 may belocated or disposed remotely from the controller 202, but nonethelessmay be communicatively connected to the controller 202, e.g., via thenetwork 108 and optionally the network 132. For example, at least aportion of the data storage entities 210 may be implemented as a remotedata bank or data cloud storage. It is noted that although more than onedata storage entity 210 may be included in the real property monitoringsystem 100, for ease of reading, the data storage entities 210 arereferred to herein using the singular tense, e.g., the database 210 orthe data storage entity 210.

At any rate, the database 210 may be adapted to store data related tothe operation of the real property monitoring system 100. Such datamight include, for example, telematics data collected by the intelligentmonitoring system controller 106 from the intelligent building products110, 112, 114, 116, 118 pertaining to the real property monitoringsystem 100 such as sensor data, power usage data, control data, inputdata, other data pertaining to the usage of the intelligent buildingproducts, user profiles and preferences, and/or other types of data.Generally speaking, the data stored in the database 210 may includetime-series data, where each time-series data value is associated with arespective timestamp or other suitable indication of a particular timeat which the data value was collected and/or stored. The intelligentmonitoring system controller 106 may access data stored in the database210 when executing various functions and tasks associated with theoperation of the real property monitoring system 100.

The intelligent monitoring system controller 106 may use the monitoringapplication 222 to receive and process data that is generated by theintelligent building products 110, 112, 114, 116, 118. For example, dataindicative of sensed conditions may be transmitted from the sensors 112to the monitoring application 220, which may then store the receiveddata, process the received data (e.g., in conjunction with other datareceived from other intelligent building products), and take anyresulting actions based upon the processed data, such as activatingalarm, notifying an end-user, controlling another intelligent buildingproduct or component of the building 130, etc.

The intelligent monitoring system controller may use the graphical userinterface 220 to provide, e.g., on the display 116 and/or on the remotedisplay 116R, information based upon the data received from theintelligent building products 110, 112, 114, 116/116R, 118/118R. Forexample, the intelligent monitoring system controller 106 may beconfigured to provide with the display 116 and/or remote display 116Rthe state of one or more control devices 110 (e.g., whether a light ison or off), a reading from a sensor 112 (e.g., whether water has beendetected in the basement), the state of or a reading from an appliance114 (e.g., whether the stove is on), etc. Additionally, oralternatively, the intelligent monitoring system controller 106 may usethe graphical user interface 220 to provide, e.g., on the display 116and/or remote display 116R, with alerts generated from the data receivedfrom the intelligent building products 110, 112, 114, 116, 118 such as,for example, a security system alert, a fire alert, a flooding alert,power outage alert, etc.

The end-user may acknowledge the information provided, disable alerts,forward an alert to a monitoring service and/or to authorities, adjustthe state of a control device 110, adjust the state of an appliance 114,etc. using the display 116 and/or remote display 116R in conjunctionwith an input device 118 and/or remote input device 118R. For example,an end-user may receive an alert that the security system in thebuilding 130 has been activated on the user's smartphone. Using his orher smartphone, the end-user may disable the alert or forward the alertto a monitoring service or to local authorities. In another example, anend-user may use his or her tablet computer to check to see if s/heremembered to turn off the stove. The tablet computer may access theintelligent building controller 106 over the network 132 to query thecurrent state of the stove. If s/he sees that the stove is on, s/he mayinput a command on the tablet computer to deactivate the stove. Ofcourse, it will be understood that the foregoing are but two examples.

Alternatively or additionally, the intelligent monitoring systemcontroller 106 may send the information based upon the data receivedfrom the intelligent building products 110, 112, 114, 116, 118 to theserver 140 over the network 132, and the server 140 may be configured toprovide the information with the display 116 and/or remote display 116R.In such cases, the server 140 may act as a middleman between theintelligent building controller 106 and the display 116 and/or remotedisplay 116R.

Referring again to FIG. 1, as an alternative to or in addition to theintelligent monitoring system controller 106, a remote intelligentmonitoring system controller 106R may be used to replace or augment thefunctions of the intelligent monitoring system controller 106. Theremote intelligent monitoring system controller 106R may be a computersystem or server connected to the network 132 by one or more data and/orcommunications links 128, and may generally have an architecture similarto that of the intelligent monitoring system controller 106 shown inFIG. 2. Further, in one embodiment, the remote intelligent monitoringsystem controller 106R may be implemented using distributed processingor “cloud computing” where the functions of the remote intelligentmonitoring system controller 106R are performed by multiple computers orservers connected to the network 132. In one embodiment, the remoteintelligent monitoring system controller 106R may be implemented in oneor more servers 140 included in the back-end components 104 or in asimilar server arrangement included in the front-end components 102.

Again referring to FIG. 1, a control device 110 may be any of a numberof devices that allow automatic and/or remote control of components orsystems at the building 130. For example, the control device 110 may bea thermostat that can be adjusted according to inputs from theintelligent monitoring system controller 106 to increase or decrease thetemperature in the building 130. Such a thermostat may control thetemperature in a room and/or the entire building 130. The control device110 may also be a light switch that can be adjusted according to inputsfrom the intelligent monitoring system controller 106 to turn on, turnoff, brighten, and/or dim lights in the building. Such light switchesmay be coupled to all the lights in a room and/or an individual lightfixture.

The control device 110 may be an automated power outlet that can beadjusted according to inputs from the intelligent monitoring systemcontroller 106 to apply power and/or remove power from an outlet. Suchan automated power outlet may, for example, allow for remote turning offof a television that was left on with a user command, automatic turningoff of an electric stove that was left on after a threshold amount oftime has elapsed since motion was detected in the building 130,automatic turning on of a lamp when motion is detected in the room, etc.

Similarly, the control device 110 may be an automated circuit breakerthat can be adjusted according to input from the intelligent monitoringsystem controller 106 to automatically and/or remotely apply or removepower to the entire building 130. The control device 110 may be anautomated water valve that can be adjusted according to inputs from theintelligent monitoring system controller 106 to adjust the flow of waterin and around the building 130 (e.g., turning on or turning offsprinklers, turning on a pump to prevent the basement from flooding,etc.).

The control device 110 may be an automated gas valve that can beadjusted according to input from the intelligent monitoring systemcontroller 106 to adjust the flow of gas in and around the building 130.Such an automated gas valve may, for example, allow for automatic and/orremote shutting off of gas during a fire or earthquake, etc. Of course,other control devices 110 may be included in the real propertymonitoring system 100.

The sensor 112 may be any of a number of sensors that may gatherinformation about conditions in or around the building 130 and/oractivities in or around the building 130. That is, one or more sensors112 may monitor respective dynamic, physical characteristics and/orconditions associated with the building 130 and/or its internal and/orexternal environment. For example, the sensor 112 may be a smokedetector which may send an input to the intelligent monitoring systemcontroller 106 indicating the presence of smoke in the building 130. Thesensor 112 may also be a part of the thermostat discussed above whichmay send input to the intelligent monitoring system controller 106indicating the temperature in the building 130.

The sensor 112 may be a water sensor which may send input to theintelligent monitoring system controller 106 indicating, for example,the flow rate of a faucet, the presence of water in the basement, a roofleak in the attic, whether the sprinkler system is turned on, etc. Thesensor 112 may be an energy monitor which may measure the power usage ofa light fixture, an appliance, an entire room, the entire building 130,etc.

The sensor 112 may be any of a number of security sensors. Such securitysensors may include motion sensors, door sensors (to detect the opening,closing, and/or breaking of a door), window sensors (to detect theopening, closing, and/or break of a window), etc. The sensor 112 may bea camera and/or a microphone which may send visual and/or audible inputto the intelligent monitoring system controller 106.

The appliance 114 may be any of a number of appliances that may bepresent in the building 130 and communicating with the intelligentmonitoring system controller 106. Each appliance 114 may be a “smart”appliance. For example, the appliance 114 may have an integratedcomputer system that helps to optimize the operation of the appliance114. Such an integrated computer system may assist, for example, withscheduling usage of the appliance (e.g., a smart dishwasher that willwait to run the dishwashing cycle until off-peak hours), sending usagereports to the intelligent monitoring system controller 106, sendingsensor data to the intelligent monitoring system controller 106,receiving commands from the intelligent monitoring system controller106, etc.

An appliance 114 may be a refrigerator, dishwasher, a washing machine, adryer, an oven, a stove, a microwave, a coffeemaker, a blender, a standmixer, a television, a video game console, a cable box or digital videorecorder, an air conditioning unit or system, a dishwasher, etc.Additionally, an appliance 114 may also be a household robot (e.g., arobotic vacuum cleaner).

The display 116 may be any of a number of visual and/or audible outputdevices that may be used to display output from the intelligentmonitoring system controller 106. Such output may include sensorreadings, alert messages, reports on the usage of various system in thebuilding (e.g., electricity, water, etc.), a list of supplies topurchase (e.g., a smart refrigerator has reported that the milk and eggsare running out and recommends to purchase some of each), video orimages from a camera, a user interface operating in conjunction with theinput device 118, etc. The display 116 may also display data generatedoutside the building 130, such as information about weather conditions,public safety announcements, sports scores, advertisements, televisionchannels, videos, etc.

The display 116 may be a monitor (e.g., an LCD monitor, a CRT monitor),a television, a screen integrated into a control panel of theintelligent monitoring system controller 106, a screen integrated intoan appliance 114, etc. The display 116 may be used to present agraphical user interface 220 with which the end-user can interact withthe intelligent monitoring system controller 106. Additionally, thedisplay 116 may also include or be connected to speakers (not shown).Such speakers may be used to present information from the intelligentmonitoring system controller 106, for example, in connection with thegraphical user interface 220, an audible alert, etc.

The display 116 may also be a display that is remote from the building130. The display 116 may be a remote display 116R (e.g., a smartphone,tablet computer, or personal computer, etc.) that sends and receivesinformation over the network 132 over one or more wireless connectionsor links 124 (e.g., a cellular network connection, an 802.11 connection,and/or other type of data or communications connection or link), and/orover one or more wired data and/or communications connections or links126.

The remote display 116R may include a user interface to displayinformation about the intelligent monitoring system to a user via anapplication installed on the smartphone, tablet computer, or laptopcomputer. The remote display 116R may receive information from theintelligent monitoring system controller 106 and display informationabout one or more of the control device 110, sensor 112, appliance 114,display 116, or input device 118. For example, a user may use theapplication on his smartphone to receive an alert from the intelligentmonitoring system controller 106 over the wireless connection(s) 124. Ofcourse, it will be understood that devices other than a smartphone,tablet computer, or personal computer may be a remote display 116R.

The input device or user interface 118 may be any of a number of inputdevices or user interfaces that may be used to input data and/orcommands to the intelligent monitoring system controller 106. Forexample, the input device 118 may be a keyboard, mouse, remote control,etc. The input device 118 may also be integrated with the display 116,for example, as a touchscreen. The input device 118 may also be amicrophone which can receive verbal commands from a user. The inputdevice 118 may be used to receive commands in connection with thegraphical user interface 220, the intelligent monitoring systemapplication 222, and/or any other applications or routines associatedwith the exemplary real property monitoring system 100.

The input device 118 may be a remote input device 118R (e.g., asmartphone, tablet computer, or personal computer, etc.) that sends andreceives information over the network 132 over one or more wirelessconnections 124 (e.g., a cellular network connection, an 802.11connection, and/or another type of wireless, data and/or communicationsconnection or link), and/or over one more wired connections or links126. The remote input device 118R may receive user input via anapplication installed on the smartphone, tablet computer, or laptopcomputer that may present a user interface to display information aboutthe intelligent building system and receive user input. The remote inputdevice 118R may send commands (e.g., activate, deactivate, toggle, etc.)to the intelligent monitoring system controller 106 to affect one ormore of the control device 110, sensor 112, appliance 114, display 116,or input device 118. For example, a user may use the application on hissmartphone to turn off his stove over the wireless connection(s) 124. Ofcourse, it will be understood that devices other than a smartphone,tablet computer, or personal computer may be a remote input device 118R.

The front-end components 102 may communicate with the back-endcomponents 104 via the network 132. For example, the intelligentmonitoring system products 106-118 situated at the building 130 may becommunicatively connected to the network 132 via the network 108 and oneor more network interfaces 121 supporting one or more data and/orcommunication links 122. The one or more links 122 may include one ormore wired communication or data links and/or one or more wirelesscommunication or data links, and as such, the one or more networkinterfaces 121 may include one or more physical ports and/or one or morewireless transceivers. The remote products 106R, 116R, 118R may besimilarly connected to the network 132 over respective data and/orcommunication links 124, 126, and 128.

The network 132 may include one or more proprietary networks, the publicInternet, one or more virtual private networks, or some other type ofnetwork, such as dedicated access lines, plain ordinary telephone lines,satellite links, data links, communications links, combinations ofthese, etc. Where the network 132 comprises an internet (either privateand/or public), data communications may take place over the network 132via an Internet communication protocol.

The back-end components 104 may include a server 140. The server 140 mayinclude one or more computer processors adapted and configured toexecute various software applications and components of the realproperty monitoring system 100, in addition to other softwareapplications. Although the server 140 is depicted in FIG. 1 as being asingle computing device, it is understood that the server 140 maylogically be implemented using multiple computing devices, such as aserver bank or a computing cloud.

Similarly to the intelligent monitoring system controller 106, theserver 140 may have a controller 155 that is operatively connected to adatabase 146 via a link 156. It should be noted that, while not shown,additional databases may be linked to the controller 155 in a knownmanner. The controller 155 may include a program memory 160, a processor162 (may be called a microcontroller or a microprocessor), arandom-access memory (RAM) 164, and an input/output (I/O) circuit 166,all of which may be interconnected via an address/data bus 165.

It should be appreciated that although only one microprocessor 162 isshown, the controller 155 may include multiple microprocessors 162.Similarly, the memory of the controller 155 may include multiple RAMs164 and multiple program memories 160. Although the I/O circuit 166 isshown as a single block, it should be appreciated that the I/O circuit166 may include a number of different types of I/O circuits.

The RAM(s) 164 and program memories 160 may be implemented assemiconductor memories, magnetically readable memories, biologicallyreadable memories, and/or optically readable memories, for example. Thecontroller 155 may also be operatively connected to the network 132 viaone or more network interfaces 134 supporting one or more data and/orcommunications links 135, which may include any number of wirelessand/or wired communication or data links. As such, the one or morenetwork interfaces 134 may include one or more physical ports and/or oneor more wireless transceivers.

The server 140 may include and/or may be communicatively connected to(e.g., via the link 156) to one or more databases 146 or other datastorage mechanisms or entities 146 (e.g., one or more hard disk drives,optical storage drives, solid state storage devices, etc.), which maycomprise one or more respective, non-transitory, tangiblecomputer-storage media. In one embodiment, at least one of the datastorage entities 146 is local to the controller 155 and, in someimplementations, may be included with the controller 155 in an integraldevice.

In one embodiment, at least one of the data storage entities 146 may belocated or disposed remotely from the controller 155, but nonethelessmay be communicatively connected to the controller 155, e.g., via thenetwork 132. For example, at least a portion of the data storageentities 146 may be implemented as a remote data bank or data cloudstorage. It is noted that although more than one data storage entity 146may be included in the intelligent monitoring system 100, for ease ofreading herein, the data storage entities 146 are referred to hereinusing the singular tense, e.g., the database 146 of the data storageentity 146.

The database 146 may be adapted to store data related to the operationof the real property monitoring system 100. Such data might include, forexample, telematics data collected by the intelligent monitoring systemcontroller 106 pertaining to the real property monitoring system 100 anduploaded to the server 140, such as data pertaining to the usage of theintelligent building products, data pertaining to third-party input andits processing (e.g., by the information processor 226), data pertainingto detected damage associated with real property, user and/or customerprofiles, information about various intelligent building products thatare available for installation, web page templates and/or web pages, orother kinds of data. The server 140 may access data stored in thedatabase 146 when executing various functions and tasks associated withthe operation of the real property monitoring system 100.

As shown in FIG. 1, the program memory 160 and/or the RAM 164 may storevarious applications for execution by the microprocessor 162. Forexample, a user-interface application 236 may provide a user interfaceto the server 140. The user interface application 236 may, for example,allow a network administrator to configure, troubleshoot, or testvarious aspects of the server's operation, or otherwise to accessinformation thereon.

A server application 238 operates to transmit and receive informationfrom one or more intelligent monitoring system controllers 106 on thenetwork 132. The server application 238 may receive and aggregate alertsand usage data, and forward alerts to a remote system monitor 142, e.g.,via one or more data and/or communication links 145. The serverapplication 238 may be a single module 238 or a plurality of modules238A, 238B. While the server application 238 is depicted in FIG. 1 asincluding two modules, 238A and 238B, the server application 238 mayinclude any number of modules accomplishing tasks related toimplantation of the server 140.

By way of example, the module 238A may populate and transmit the clientapplication data and/or may receive and evaluate inputs from theend-user to receive a data access request, while the module 238B maycommunicate with one or more of the back-end components 104 to fulfill adata access request or forward an alert to a remote system monitor 142.In one embodiment, at least a portion of or the entire monitoring module224 of FIG. 2 may be included in the server application 238 (not shown).Additionally or alternatively, at least a portion of or the entiredamage detection module 226 of FIG. 2 may be included in the serverapplication 238 (also not shown).

Additionally, the back-end components 104 may further include theintelligent, remote monitoring system monitor 142. The remote systemmonitor 142 may be a human monitor and/or a computer monitor as shown inFIG. 1. The remote system monitor 142 may receive data from the server140 and/or the front-end components 102 over the network 132, e.g., viathe link(s) 145, which may comprise any number of wired and/or wirelessdata and/or communications links. Such data may include information fromand/or about the intelligent building controller 106, control device110, sensor 112, appliance 114, display 116, and/or input device 118.

The remote system monitor 142 may also receive this informationindirectly (e.g., the server 140 may forward information to the remotesystem monitor 142, the end-user may forward alerts to the remote systemmonitor 142 with an input device 118 or remote input device 118R). Ifthe remote system monitor 142 receives information indicating an eventpotentially requiring an appropriate responder or authority (e.g., lawenforcement for a security alert, fire department for a fire alert,paramedics for a medical alert, plumber for a leak alert, power companyfor a power outage alert, etc.), the remote system monitor 142 mayattempt to contact one of the authorized end-users (e.g., with atelephone call, text message, email, app alert, etc.) to verify theevent potentially requiring an appropriate responder and/or notify theappropriate responder. For example, the remote system monitor 142 mayreceive information from a smoke detector (i.e., a sensor 112)indicating that the building 130 may be ablaze.

The remote system monitor 142 may then attempt to contact the end-userto ascertain the severity of the fire and ask if the fire departmentshould be called. If none of the end-users answer or if an end-userrequests that the fire department be notified, the remote system monitor142 may contact the fire department and provide the fire dispatch withinformation about the building 130 (e.g., address, number of residents,configuration of building, etc.) and/or information about the fire(e.g., smoke detected in four rooms of the house).

In another example, the remote system monitor 142 may receiveinformation from water valve (i.e., a control 110) indicating that thevalve is open and may also receive information from a water sensor(i.e., a sensor 112) indicating that the basement has begun to flood.The remote system monitor 142 may attempt to contact one of theauthorized end-users to notify the user and ask if remote closing of thewater valve and/or calling a plumber is requested. If none of theend-users answer, or if the user responds in the affirmative, the remotesystem monitor 142 may close the water valve and/or call a plumber toprevent further flooding of the basement. It may be advantageous to callthe appropriate responder without first attempting to contact end-users(e.g. if the user has indicated he or she will be out of the country orin the wilderness).

Although the real property monitoring system 100 is shown to include oneserver 140, one remote system monitor 142, one building 130, oneintelligent monitoring system controller 106, one control device 110,one sensor 112, one appliance 114, one display 116, and one input device118, it should be understood that different numbers of servers 140,monitors 142, buildings 130, intelligent monitoring system controllers106, control devices 110, sensors 112, appliances 114, displays 116, andinput devices 118 may be utilized. For example, the system 100 mayinclude a plurality of servers 140 and hundreds of buildings 130, all ofwhich may be interconnected via the network 132.

Further, each building 130 may include more than one of each of anintelligent monitoring system controller 106, a control device 110, asensor 112, an appliance 114, a display 116, and an input device 118.For example, a large building 130 may include two intelligent monitoringsystem controllers 106 that are connected to multiple control devices110, multiple sensors 112, multiple appliances 114, multiple displays116, and/or input devices 118.

Additionally several buildings 130 may be located, by way of examplerather than limitation, in separate geographic locations from eachother, including different areas of the same city, different cities, ordifferent states. Furthermore, the processing performed by the one ormore servers 140 may be distributed among a plurality of servers in anarrangement known as “cloud computing.” According to the disclosedexample, this configuration may provide several advantages, such as, forexample, enabling near real-time uploads and downloads of information aswell as periodic uploads and downloads of information.

Turning now in particular to the local monitor 224 and the damagedetector 226, as previously discussed, at least a portion of each ofthese components may be included in the front-end components 102 (e.g.,in the controller 106 and/or the controller 106R), and/or at least aportion of each of these components may be included in the back-endcomponents 104 (e.g., in the server 140). In one embodiment, forexample, a first portion of one of the components 224, 226 may beincluded in the front-end components 102, while another portion of theone of the components 224, 226 may be included in the back-endcomponents 104. In one embodiment, for example, the entirety of one ofthe components 224, 226 (e.g., the local monitor 224) may be included inthe front-end components 102, and the entirety of another one of thecomponents 224, 226 (e.g., the damage detector 226) may be included inthe back-end components 104. Of course, other arrangements may bepossible.

The local monitor 224 may implement functionality for monitoring one ormore dynamic, physical characteristics and/or conditions associated withthe building 130, e.g., of the building 130 and/or of its internaland/or external environment. As illustrated in FIG. 1, the local monitor224 may be communicatively connected to one or more intelligent buildingproducts, e.g., one or more control devices 110, one or more sensors112, one or more appliances 114, one or more displays 116, one or moreuser interfaces 118, etc., and data generated by the intelligentbuilding products 110-118 may be transmitted to the local monitor 224.

Generally speaking, but not necessarily, data generated by theintelligent building products 110-118 may be time-series data where eachdata point includes a value and a corresponding indication of time atwhich the value was collected, observed, or generated by the respectiveintelligent building product. Control devices 110 may generate dataindicative of changes of state of various devices at the building 130,such as on/off, opened/closed, degree or amount (e.g., of temperaturefor thermostat, of amount of light for a light dimmer, of airflow for afan, etc.), and/or other changes of state of the various devices.Additionally or alternatively, control devices 110 may generate dataindicative of a control command that changed a device state, e.g.,manual or automatic adjustment of a thermostat, turning sprinklers onand off, etc. Sensors 112 may generate data indicative of a sensedcharacteristic or condition such as, for example, motion, heat, light,water, smoke, etc.

Generally speaking, sensors 112 detect or sense various dynamiccharacteristics and/or conditions of the building 130 and/or of itsinternal and/or external environment, and in some cases, a degree oramount of the dynamic characteristic (e.g., temperature, flow, density,etc.). Appliances 114 may generate data that is indicative of theoperation of the appliances 114, such as usage reports, appliance sensordata, and the like. Additionally or alternatively, appliances 114 maygenerate data indicative of a received command, such as a manual orautomatic command to turn a particular appliance on or off, to adjust acontrol on the appliance, etc.

Displays 116 and/or user interfaces 118 may generate data indicative ofuser input and/or responses that are received. Generally speaking,dynamic characteristics of the building 130 that are monitored by theintelligent building products 110, 112, 114, 116/116R, 118/118R may beindicative of the usage of the building 130, and/or of the usage and/oroperations of components and various systems (e.g., appliances, securitysystem, smart utility systems, HVAC systems, communication networksystems, etc.) that are included in and that service the building 130.

At any rate, the local monitor 224 may receive data generated by theintelligent building products 110-118 (e.g., data descriptive of variousdynamic characteristics of and/or associated with the building 130) andmay store the received data into the database 210 and/or the database146. In some scenarios, the local monitor 224 may process the datagenerated by the intelligent building products 110-118 to determine oneor more current conditions associated with the building 130, andoptionally one or more resulting actions in response to the determinedconditions. For example, alerts or alarms may be sent to the remotesystem monitor 142 based upon data generated by motion detectors, smokedetectors, etc.

Additionally or alternatively, a user may be notified of a detectedcondition, e.g., via a display 116 or user interface 118 at the building130, and/or via a remote device 116R/118R. Other actions may bepossible. The local monitor 224 may store determined and/or detectedconditions and/or any resulting actions into the database 210 and/or thedatabase 146, e.g., as time-series data.

The damage detector 226 may also implement functionality for receivingand data processing third-party input or data, and utilizing such datato detect or determine damage and/or other conditions associated withthe building 130. Third-party input or data may include digitizedinformation, such as digital images, notes, text, numbers, and/or dataof any suitable digital format.

Typically, the content of third-party input or data is generated by aparty that or who is not an end-user (e.g., owner, property manager,resident, staff, etc.) of the real property monitoring system 100 and,in some situations, may not be associated with the building 130. Forexample, a third-party may be an agent, adjuster, call-centerrepresentative image-capturing drone, or other representative of aninsurance provider of an insurance policy providing coverage for thebuilding 130, and the notes and/or images generated by therepresentative of the insurance provider (e.g., during the processing ofan insurance claim and/or during a phone or email conversation) may beconverted into a digital format and provided to the real propertymonitoring system 100 as third-party input.

In some scenarios, third-party input provided by representative of aninsurance provider may be included in a file of an insurance claim, orotherwise attached thereto. A third-party may be a reporting agency,such as a news reporting agency, a weather service, local authorities,etc. Accordingly, third-party input provided by such sources mayinclude, for example, maps, police reports, incident reports, and thelike.

Additionally, the damage detector 226 may generate and/or obtain dynamiccharacteristic data indicative of various dynamic characteristics thathave occurred at the building 130 and optionally their times ofoccurrence, frequencies, magnitudes, etc. The dynamic characteristicdata that is associated with the building 130 may be generated, forexample, based upon signals provided by the sensors 112 of the realproperty monitoring system 100, and optionally by other intelligentbuilding products 110, 114, 116/116R, 118/118R.

At least some of the dynamic characteristic data may be provided to thedamage detector 226 by the Local Monitor 224. Additionally oralternatively, the damage detector 226 may itself generate at least aportion of the dynamic characteristic data, and/or the damage detector226 may read or access at least a portion of the dynamic characteristicdata from the data storage area 146, 210

Moreover, damage detector 228 may implement functionality fordetermining and/or detecting damage to the building 130 and/or otherconditions associated with the building 130 using the third-party inputand the dynamic characteristic data of the building 130, and therebydiscovering and or determining one or more conditions associated withthe building 130 that, for example, otherwise would not be characterizedand/or even detected using only sensor-generated data 112 and/or thehuman eye. Specifically, in one implementation, the damage detector 228may use the third-party input and the dynamic characteristic data of thebuilding 130 to train a model, e.g., a statistical or analytical model,which may be stored in the data storage area 146, 210. The model may bepredictive of one or more conditions that may be associated with thebuilding 130, for example.

The damage detector 228 may apply the trained model to the dynamiccharacteristic data of the building 130 and/or to another set of dynamiccharacteristic data of the building 130. Outputs of the application ofthe trained model may indicate one or more conditions associated withthe building 130 that are more strongly correlated with the building 130than are other conditions. For example, the application of the trainedmodel may indicate or discover one or more conditions that areassociated with both the building 130 and the impacting event which wasdescribed by the content of the third-party input.

In one embodiment, particular damage to the building 130, e.g. that isat least in part caused by the impacting event, may be determined ordiscovered by using the trained model. Additionally or alternatively,other conditions associated with the building 130 such as, for example,causes of loss, quantified risk levels, adjustments to insurancepolicies, etc., may be determined or discovered by using the trainedmodel. The damage detector 228 may provide an indication of one or morediscovered conditions corresponding to the building 130 to othercomputing devices, to user interfaces, or to other systems, e.g., viathe network 132.

Exemplary Computer-Implemented Method

FIG. 3 depicts a flow diagram of an exemplary computer-implementedmethod 300 of a method for monitoring a building and/or detecting damageand other conditions at the building. At least a portion of the method300 may be performed, for example, by one or more components of the realproperty monitoring system 100 of FIGS. 1 and 2, and/or by othersuitable devices, apparatuses, and/or systems. For example, at least aportion of the method 300 may be performed by the local monitor 224and/or by the damage detector 226 of the system 100. Additionally oralternatively, at least a portion of the method 300 may be performed bythe front-end components 102 and/or the back-end components 104 of thesystem 100. For ease of illustration herein, the method 300 is discussedwith simultaneous reference to FIGS. 1 and 2.

As shown in FIG. 3, the method 300 may include monitoring (block 302) aplurality of dynamic, physical characteristics associated with abuilding. For example, referring to FIGS. 1 and 2, a plurality ofsensors 112 of a real property monitoring system 100 may be utilized tomonitor one or more dynamic, physical characteristics of or associatedwith the building 130. The plurality of sensors 112 may generate signalsindicative of sensed, respective dynamic, physical characteristicsassociated with the building 130, such as movement, motion, temperature,moisture, humidity, presence of smoke and/or gas, on/off (e.g., ofvarious devices, appliances, etc.), open/closed (e.g., of variouswindows, doors, etc.), and the like. The plurality of sensors 112 may befixedly disposed at respective locations at the building 130 and/or inits environment (e.g., on the interior of the building, on the exteriorof the building, on a fixture disposed on a parcel of land or other realestate on which the building is located, etc.), and at least some of theplurality of sensors 112 may be fixedly attached to the building 100.

In one embodiment, monitoring the plurality of dynamic, physicalcharacteristics of the building (block 302) may additionally includeutilizing one or more controls 110, appliances 114, displays 116, and/oruser interfaces 118 (e.g., one or more intelligent building products) ofthe system 100 to monitor at least some of the dynamic, physicalcharacteristics, where the intelligent building product(s) 110, 114,116, 118, 118R generate respective signals indicative of one or moredynamic, physical characteristics associated with the building 130. Thesignals generated by the sensors 112 (and optionally by the intelligentbuilding products 110, 114, 116, 118, 118R) may be transmitted to themonitoring controller 106, the remote monitoring controller 106R, and/orthe server 140.

Based upon the signals generated by the sensors 112 (and optionally bythe intelligent building products 110, 114, 116, 118, 118R), dynamiccharacteristic data that is indicative of the plurality of dynamic,physical characteristics that are associated with the building 130 andthat are being monitored by the plurality of sensors (and optionally bythe intelligent building products 110, 114, 116, 118, 118R) may begenerated and stored (block 305). For example, the intelligentmonitoring application 222 may process the received signals (eitherindividually, or in combination with other signals) to generate thedynamic characteristic data, and the dynamic characteristic dataassociated with the building 130 may be stored in a data storage entitythat is included in the real property monitoring system 130 and that iscommunicatively connected to monitoring controller 106, the remotemonitoring controller 106R, the server 140, the plurality of sensors112, and/or to one or more of the intelligent building product(s) 110,114, 116, 118, 118R (such as the data storage entities 146 and/or 210shown in FIGS. 1 and 2, respectively).

Generally, the dynamic characteristic data is indicative of detected,various dynamically occurring physical conditions inside of, outside of,on, at, or near the building 130, and/or respective measurements,amounts, or other indication of magnitudes of the dynamically occurring,physical conditions associated with the building 130. The dynamicconditions may include, for example, dynamic conditions of a part orcomponent of the building 130, or dynamic conditions to which the partor component of the building 130 is subjected. For example, thefoundation of the building may be subjected to rising ground waters (adetectable dynamic condition associated with the building), and thefoundation itself may suffer structural damage due to the exposure torising ground waters (another detectable dynamic condition associatedwith the building).

Additionally or alternatively, the dynamic conditions may includedynamic conditions of an object that is disposed inside, on top of, onthe property of, or otherwise near the building 130, and/or dynamicconditions to which such an object is subjected. For example, anelectric kitchen oven may be subject to a power surge, and the oven mayshort out due to the power surge, both of which are examples of dynamicconditions associated with the building. In some scenarios, at least aportion of the dynamic characteristic data may be time-series data, andas such may include timestamps or other indications of respectivetimes/dates at which the detected and/or measured dynamic conditionswere observed or detected.

At a block 308, the method 300 may include receiving input that has beengenerated by a third-party, where the third-party input includesdigitized information that is descriptive of an event that impacts thebuilding 130, e.g., an “impacting event.” Generally speaking, but notnecessarily, an event that impacts the building 130 may not be able tobe detected, described, and/or characterized (e.g., sufficientlycharacterized or described) only by the intelligent building products ofthe building 130 (e.g., the sensors 112, control(s) 110, appliance(s)114, display(s) 116/116R, and/or user interface(s) 118, 118R). Indeed,in some situations, the intelligent building products 112, 110, 114,116/116R, 118/118R of the building 130 may remain ignorant of theoccurrence of the event impacting the building 130.

Some types of impacting events may be caused or precipitated by an actorand/or other factors that are external to and independent of thebuilding 130 (e.g., aside from the impacting event, the actor/otherfactors causing the impacting event do not have a relationship orassociation with the building 130). Examples of such types of impactingevents include environmental, situational, and/or weather-related eventsthat occur in the area in which the building 130 is located, such ashurricanes, floods, wildfires, riots, earthquakes, manufacturing plantexplosions, train derailments, etc. Other examples of such impactingevents include events that are particular to the building 130, such asan out-of-control vehicle running into the building, a malfunctioningdrone that falls onto the building or is propelled through a window ofthe building, a failure of a gas or water pipe that delivers utilitiesto the building, etc. Some types of events that impact the building 130may be caused or precipitated by objects or people inside or around thebuilding 130, for example, a clothes dryer that catches on fire, a treethat falls on the roof of the building, a person who slips and fallsdown a staircase or the front steps of the building 130, etc.

At any rate, the digitized information included in the third-party inputmay be of any suitable digital or digitized format or formats, such asdigital notes and/or text (e.g., free-form notes and/or text), images,numbers, files, and/or other digital data formats. Similar to thedynamic characteristic data, the third-party input data may bedescriptive and/or indicative of the impacting event and/or of variouscharacteristics of the impacting event, and optionally respectivemeasurements, amounts, or indications of magnitudes of various portionsof the event. Respective timestamps may capture the dates/times at whichthe various third-party input data points were collected or observed.

Typically, the third-party that generates or provides the third-partyinput that is descriptive of the impacting event is not a buildingowner, building property manager, resident, tenant, or other end-user ofthe monitoring system 100. As such, the third-party input data mayidentify and/or characterize various aspects of the event from aperspective that is different than that which is able to be sensed bythe intelligent building products of the building 130 and/or that isdifferent than the immediate experiences and observations of end-usersof the building 130. For example, sensors 112 at the building 130 maydetect a high wind speed, while the third-party input may describe atornado, and thus the resulting high wind speeds detected by the sensors112. In another example, sensors 112 at the building 130 may detectrising waters in the basement, while the third-party input may describea break in a levee.

The content of the third-party input may be generated or provided by oneor more different third-parties. For example, a third-party may be anagent, adjustor, call-center representative, image-capturing drone, orother representative of an insurance provider of an insurance policy forthe building 130. Input provided by such types of third-parties may beprovided in real-time, on demand, and/or in conjunction with aninsurance claim associated with the building 130, e.g., when maintainedin or attached to a file of the insurance claim.

A third-party may be another party who is not an end-user of themonitoring system 100 of the building 130 and who is not arepresentative of the building's insurance provider. For example, areport on a travel path and strength of a hurricane provided by theNational Weather Service, a police report indicating the path of arunaway vehicle, and a map of where and when city-wide power outagesoccurred may be considered to be third-party input. The third-partyinput may be received (block 308) via one or more network interfaces121, 134 of the real property monitoring system 100 and, in somescenarios, via the network 132.

At a block 310, the method 300 may include training a model based uponthe dynamic characteristic data associated with the building 130 and thethird-party input. The model may be, for example, a statistical oranalytical model, which may be a publicly-available or proprietarymodel. The model may be predictive of one or more conditions that may beassociated with the building 130. For example, the one or moreconditions associated with the building 130 may include particulardamage at the building 130 that was caused by the occurrence of theimpacting event, and that otherwise would not be discoverable via humanobservation or investigation and, in some scenarios, would not bediscoverable via the intelligent building products 110, 112, 114,116/116R, 118/118R of the building 130. For instance, the specificdamage to circuits, pipes, and other building support systems that arepositioned between walls of the building and that are not beingmonitored by any intelligent building products 110, 112, 114, 116/116R,118/118R may be discovered and quantified using the model, withoutrequiring any human, physical investigation such as opening up thewalls.

In one embodiment, the model may be trained (block 310) using thedynamic characteristic data associated with the building 130, thethird-party input, and additional types of data. For example, the modelmay be trained by utilizing the dynamic characteristic data of thebuilding 130, the third-party input, and static characteristic dataassociated with the building 130. Generally speaking, staticcharacteristic data associated with the building 130 may include datathat is descriptive or indicative of one or more static characteristicsof the building 130 such as, for example, a type of the building (e.g.,ranch, Cape Cod, apartment building, storage warehouse, etc.), amaterial or product used to construct the building (e.g., roofing,insulation, concrete, vapor barriers, etc.), a make, model, and/or yearof an appliance inside the building, the grading of the parcel of landon which the building is located, and other static characteristics.

Additionally or alternatively, the model may be trained (block 310) byutilizing historical insurance claim data, which may pertain to thebuilding 130 and/or may pertain to other buildings. Historical insuranceclaim data may include indications, for example, of whether or not aninsurance claim was paid; costs of material and/or labor for replacementor repair; types of injuries, where treated, how treated, etc.;disbursements related to the claim such as hotel costs, rental carcosts, and/or other types of payouts; causes of loss; and the like.Historical insurance claim data may include indications, for example, ofstatic characteristic data and dynamic characteristic data of thebuilding 130 and/or of other buildings, third-party input related to thehistorical insurance claims, and/or any other types of data that isassociated with historical insurance claims of buildings and/or realproperties. Generally, historical insurance claim data may be obtainedfrom files or other records of insurance claims that have been filed forthe building 130 and/or for other buildings.

As such, at a block 312, the method 300 may include applying the trainedanalytics model to the dynamic characteristic data corresponding to thebuilding and/or to additional dynamic characteristic data correspondingto the building, thereby discovering particular damage to the buildingthat corresponds to the impacting event, e.g., particular damage that iscaused, at least in part, by the occurrence of the impacting event. Forexample, the nature, the location, and/or the degree of particulardamage of the building 130 may be discovered at the block 312.

In one embodiment, one or more additional conditions may also bediscovered at the block 312. For example, a cause of loss that isassociated with both the building 130 and the impacting event may bediscovered at the block 312. The discovered cause of loss may be a knowncause of loss, e.g., the discovered cause of loss is included in a setof causes of loss known to and utilized by an insurance provider toassess insurance claims (e.g., wind, fire, hail, mold, smoke, weight ofsnow or ice, freezing pipes, etc.).

In some scenarios, a discovered cause of loss may be a new cause of lossthat is excluded from the set of known causes of loss. In thesescenarios, the method 300 may include updating the set of known causesof loss to include the newly discovered cause of loss. In anotherexample, additional conditions corresponding to the building 130 thatmay be discovered at the block 312 may include adjustments to one ormore terms of an insurance policy that provides coverage for thebuilding 130. For instance, an adjustment to the pricing and/or otherfinancial terms of the insurance policy (e.g., a premium amount, adeductible amount, a coverage amount, a replacement amount, etc.) may bediscovered by applying the trained analytics model to dynamiccharacteristic data of the building 130 and to the third-party input.

The pricing and/or other financial terms of the insurance policy may beadjusted to more accurately reflect the risk, or the lack thereof,associated with the building 130, and in particular, in light of theimpacting event as described by the third-party input. As such, an ownerof the building 130 is able to obtain insurance coverage for thebuilding 130 with a policy and terms that more accurately reflect theusage of the building 130 as well as the impact of various events on thebuilding 130.

At a block 315, the method 300 may include transmitting an indication ofthe discovered condition(s), e.g., the discovered particular damage ofthe building 130, to the remote computing device and/or to a userinterface. For example, an indication of the particular damage to thebuilding 130 and/or of other conditions may be transmitted, via thenetwork 132, to the remote monitor 142, to a computing system of aninsurance provider, to a computing system of a first responder, to anend-user of the monitoring system 100, etc. The recipient computingsystem (and, in some embodiments, the real property monitoring system100 itself) may then initiate suitable actions and/or activities tomitigate the discovered condition(s).

In one embodiment (not shown in FIG. 3), the method 300 may includere-training or updating the model. For example, the model may bere-trained or updated by using the third-party input, the dynamiccharacteristic data of the building 130, and subsequently received data.Subsequently received data may include, for example, subsequentlyreceived third-party input, subsequently received dynamic characteristicdata of the building 130 and/or of other buildings, subsequentlyreceived insurance claim data of the building 130 and/or of otherbuildings, other types of data corresponding to the building 130 and/orto other buildings that is subsequently received, and/or other types ofdata corresponding to the impacting event and/or similar events that issubsequently received.

The re-trained or updated model may be then utilized to discoveradditional information which, for example, may include additionaldetail, aspects, accuracy, and/or precision to the informationdescriptive of the previously-discovered condition(s), and/or mayinclude one or more new conditions that the previous model was unable todiscover. For example, as more insurance claim data related to hurricanedamage is used to train the model, future applications of the updatedmodel are more quickly and accurately able to differentiate hurricanedamage from other types of wind and/or water damage.

In one embodiment, the model may be updated periodically, repeatedly, orupon demand. The updated model may then be applied to discover one ormore adjustments to an insurance policy and/or to a group of insurancepolicies, such as an adjustment to pricing and/or other insurance terms.As such, pricing models of insurance policies are able to moreaccurately reflect current risk (or lack thereof) to the buildings andother real property for which the insurance policies provide coverage.

The benefits of just-in-time, accurate risk assessment using realproperty monitoring systems may be continually adjusted and passed alongto end-users throughout the terms of their insurance policies. Moreover,insurance providers are able to better re-allocate pricing and otherinsurance terms amongst various portions of their customer base to moreefficiently mitigate overall risk. Of course, any data corresponding tothe building 130 that is collected and utilized by the real propertymonitoring system 100 would be utilized with any of the systems andmethods disclosed herein with permission or affirmative consent of theowner, tenant, property manager, and/or other end-user associated withthe building 130.

Thus, in view of the above, the systems, methods, and/or techniques (orportions thereof) disclosed herein for using a real property monitoringsystem to automatically detect damage and/or other conditions at abuilding (and in particular, to detect damage at the building caused, atleast in part, by an impacting event) enable such damage and/or otherconditions to be more quickly and accurately ascertained, discovered,and/or characterized as compared to currently known techniques. Indeed,in some scenarios, damage that was previously undetectable bynon-invasive techniques (e.g., damage that required human investigationand actions, such as cutting into walls, testing electrical circuits,etc. to detect and characterize the damage) and/or other conditions areable to be automatically (as well as quickly and more accurately)detected and identified using at least portions of the systems, methods,and/or techniques disclosed herein.

As such, risk (or lack thereof) of loss associated with the building isalso able to be automatically, quickly, and accurately identified.Accordingly, more appropriate and suitable risk mitigation techniquesmay be able to be applied at or to the building 130, e.g., in a moretimely manner, to thereby prevent additional damage and/or loss fromoccurring.

Overview of Artificial Intelligence Platform for Real Property

The embodiments described herein may relate to, inter alia, determiningan accurate, granular real property insurance risk level correspondingto a plurality of inputs. More particularly, in some embodiments, one ormore neural network models (and/or other machine learning models,modules, algorithms, or programs, or other artificial intelligencemodels, modules, algorithms, or programs) may be trained usinghistorical insurance claims data as training input. Historical insuranceclaims data may include, for example, indications of staticcharacteristic data and dynamic characteristic data of buildings and/orreal properties; third-party input related to the historical insuranceclaims; whether or not an insurance claim was paid; costs of materialand/or labor for replacement or repair; types of injuries; wheretreated, how treated, etc.; disbursements related to the claim such ashotel costs, rental car costs, and/or other types of payouts; causes ofloss; and/or other data associated with historical insurance claimscorresponding to buildings and/or real properties. Generally speaking,historical insurance claims data may be obtained from files or otherrecords of insurance claims that been filed for buildings and/or otherreal properties.

Risk levels related to building and/or real property insurance may bedetermined using the techniques described herein for any number ofassessments that are performed with respect to building and/or realproperty insurance. In an example scenario, at least some of thetechniques disclosed herein may be utilized to determine risk levelscorresponding to an application for a new insurance policy to providecoverage for a building or real property, such as during theunderwriting process and/or at other stages of processing a building areal property insurance application. In another example scenario, atleast some of the techniques disclosed herein may be utilized todetermine risk levels corresponding to a renewal or continuingeligibility of an existing insurance policy for a building or realproperty, such as during the re-underwriting process and/or other stagesof processing the renewal or continuing eligibility of the existinginsurance policy.

As such, an application for a new insurance policy for building and/orreal property insurance, a renewal or re-underwriting of an existinginsurance policy for building and/or real property insurance, orinformation associated with a claim against an existing insurance policyfor building and/or real property insurance may be provided to a clientcomputing device (e.g., a smartphone, tablet, laptop, desktop computingdevice, wearable, or other computing device) of a user. A user of theapplication, who may be an employee of a company or other entityemploying the methods described herein or a customer of that company,may enter input into the application via a user interface or othermeans. The input may be transmitted from the client computing device toa remote computing device (e.g., one or more servers) via a computernetwork, and then processed further, including by applying input enteredinto the client to the one or more trained neural network models (and/orother machine learning models, modules, algorithms, or programs) toproduce labels and weights indicating net or individual risk factors.Additionally or alternatively, input may be transmitted from a realproperty monitoring system, such as the system 100, to the remotecomputing device for additional processing by the one or more trainedneural network models (or other trained machine learning models,modules, algorithms, or programs).

For example, the remote computing device may receive the input anddetermine, using a trained neural network (or other machine learningmodel, module, algorithm, or program), one or more risk indicatorsapplicable to the input, and/or a risk level. Herein risk indicators maybe expressed numerically, as strings (e.g., as labels), or in any othersuitable format. Risk levels may be expressed as Boolean values (e.g.,risk/no risk), scaled quantities (e.g., from 0.0-1.0), or in any othersuitable format. The determined risk indicators and/or risk level may bedisplayed to the user, and/or may be provided as input to anotherapplication (e.g., to an application which uses the risk indicators andcalculated risk in an insurance quotation calculation or for otherpurposes). An insurance quotation may include a price, parametersdescribing the real property, and/or one or more identified riskindicators, among other information.

In some scenarios, additional or alternative information may begenerated (e.g., by one or more other applications) based upon thedetermined risk indicators and/or risk level, and such information maybe provided to the client computing device and/or to other computingdevices of the insurance company. Examples of additional or alternativerisk-related information which may be generated include risk mitigationimperatives or actions (and optionally respective urgencies thereof)corresponding to a building and/or real property insurance policyapplication, to an associated insurance claim, to a renewal of anexisting building and/or real property insurance policy, or to are-underwriting of an existing building and/or real property insurancepolicy may be determined based upon the determined risk indicators, andmay be provided to the client computing device and/or to other computingdevices of the insurance company. For example, the techniques describedherein may generate a risk mitigation imperative that is transmitted toa customer's mobile device, e.g., “clean out dryer vent.” In anotherexample, the techniques described herein may generate a risk mitigationimperative to an insurance provider to increase a deductible on aparticular homeowner's policy, e.g., when a customer has a highfrequency of small claims.

Other risk-related information such as a mitigation plan (which mayinclude multiple mitigation imperatives or actions, and optionallyrespective urgencies thereof), notifications, etc. may be additionallyor alternatively determined based upon the determined risk indicators,and may be provided to the client computing device and/or to othercomputing devices of the insurance company. By transmitting input to theremote computing device for processing and analysis, an accurate risklevel and/or other risk-related information may be determined based upona wealth of historical knowledge and provided to the user in what mayappear to the user to be a very rapid, even instantaneous, manner.

Exemplary Environment for Identifying Risk Factors and Calculating Riskin Data

Turning to FIG. 4, an exemplary computing environment 400,representative of artificial intelligence platform for real propertyinsurance, is depicted. The computing environment 400 may be at leastpartially included with the system 100, in some implementations.Environment 400 may include input data 402 and historical data 408, bothof which may comprise a list of parameters, a plurality (e.g., thousandsor millions) of electronic documents, or other information. As usedherein, the term “data” generally refers to information which exists inthe environment 400 and is related to a real property (e.g., a house, ahome, a building, a parcel of land, or other type of real property),e.g., input data 402 and/or historical data 408. For example, data mayinclude an electronic document representing a real property insuranceclaim, telematics information indicative of environmental conditions atand/or human usage of the real property, information related to the typeof real property and/or its characteristics and materials of which it iscomprised, and/or other information.

Data may be historical or current. Although data may be related to anongoing claim filed by an owner of real property, in some embodiments,data may consist of raw data parameters entered by a human user of theenvironment 400 or which is retrieved/received from another computingsystem, such as the real property monitoring system 100.

Data may or may not relate to the claims filing process, and while someof the examples described herein refer to real property insuranceclaims, it should be appreciated that the techniques described hereinmay be applicable to other types of electronic documents, in otherdomains. For example, the techniques herein may be applicable toidentifying risk factors in other insurance domains, such asagricultural insurance, vehicle insurance, health or life insurance,renters insurance, etc. In that case, the scope and content of the datamay differ.

As another example, data may be collected from an existing customerfiling a claim, a potential or prospective customer applying for a newinsurance policy or renewing an existing insurance policy, an insuranceprovider (e.g., the proprietor of the environment 400) renewing orre-underwriting an existing insurance policy, etc., or data may besupplied by a third party such as a company other than the proprietor ofthe environment 400. In some cases, data may reside in paper files thatare scanned or entered into a digital format by a human or by anautomated process (e.g., via a scanner). Generally, data may compriseany digital information, from any source, created at any time.

Input data 402 may be loaded into an artificial intelligence system 404to organize, analyze, and process input data 402 in a manner thatfacilitates efficient determination of risk levels by risk levelanalysis platform 406. The loading of input data 402 may be performed byexecuting a computer program on a computing device that has access tothe environment 400, and the loading process may include the computerprogram coordinating data transfer between input data 402 and AIplatform 404 (e.g., by the computer program providing an instruction toAI platform 404 as to an address or location at which input data 402 isstored). As previously discussed, input data 402 may include data thathas been entered and stored by a user (e.g., via a mobile computingdevice or other client device), and/or may include telematics datagenerated by one or more buildings or other types of real property thatis automatically received by the system 400, e.g., from one or more realproperty monitoring systems 100, and stored.

AI platform 404 may reference the address at which input data 402 isstored to retrieve records from input data 402 to perform risk leveldetermination techniques. AI platform 404 may be thought of as acollection of algorithms configured to receive and process parameters,and to produce labels and, in some embodiments, risk and/or pricinginformation.

As discussed below with respect to FIGS. 5, 6, and 7, AI platform 404may be used to train multiple neural network models (and/or othermachine learning models, modules, algorithms, or programs) relating todifferent granular segments of real properties. For example, AI platform404 may be used to train a neural network model (or other machinelearning model, module, algorithm, or program) for real properties thatare over 100 years old. In another embodiment, AI platform 404 may beused to train a neural network model (or other machine learning model,module, algorithm, or program) for use in predicting risk of realproperties located in a particular state or locality. One embodiment ofa manner in which neural networks are created and trained is describedbelow.

In the embodiment of FIG. 4, AI platform 404 may include claim analysisunit 420 (which is also interchangeably referred to herein as “inputanalysis unit 420”). Claim analysis unit 420 may include speech-to-textunit 422 and image analysis or image processing unit 424 which maycomprise, respectively, algorithms for converting human speech into textand analyzing images (e.g., extracting information from hotel and rentalreceipts). In this way, data may comprise audio recordings (e.g.,recordings made when a customer telephones a customer service center)that may be converted to text and further used by AI platform 404.Additionally or alternatively, data may include images of handwritten,typed, or printed notes (e.g., that are attached to an insurance claim,that are transcribed by an employee or other staff member, that arereceived in an email, etc.) that may be converted to text and furtherused by the AI platform 404. In some embodiments, customer behaviorrepresented in data—including the accuracy and truthfulness of acustomer—may be encoded by claim analysis unit 420 and used by AIplatform 404 to train and operate neural network models (and/or othermachine learning models, modules, algorithms, or programs).

Claim analysis unit 420 may also include text analysis unit 426, whichmay include pattern matching unit 428 and natural language processing(NLP) unit 430. In some embodiments, text analysis unit 426 maydetermine facts regarding claim inputs (e.g., the amount of money paidunder a claim). Amounts may be determined in a currency- andinflation-neutral manner, so that claim loss amounts may be directlycompared. In some embodiments, text analysis unit 426 may analyze textproduced by speech-to-text unit 422 or image analysis unit 424.

In some embodiments, pattern matching unit 428 may search textual claimdata loaded into AI platform 404 for specific strings or keywords intext (e.g., “dryer vent blocked”) which may be indicative of particulartypes of risk. NLP unit 430 may be used to identify, for example,entities or objects indicative of risk (e.g., that an injury occurred toa person, and that the person's leg was injured). NLP unit 430 mayidentify human speech patterns in data, including semantic informationrelating to entities, such as people, vehicles, homes, and otherobjects.

Relevant verbs and objects, as opposed to verbs and objects of lesserrelevance, may be determined by the use of a machine learning algorithmanalyzing historical claims. For example, both a dryer vent, occurrencesof dryer-related fires, and dates/times of general usage of the dryermay be relevant objects. Verbs indicating the setting of an alarm systemand/or the turning on and off of outside lighting may be relevant verbs.In some embodiments, text analysis unit 426 may comprise text processingalgorithms (e.g., lexers and parsers, regular expressions, etc.) and mayemit structured text in a format which may be consumed by othercomponents.

In the embodiment of FIG. 4, AI platform 404 may include a risk levelunit 440 to determine risk based upon analysis of data. Risk may becalculated with respect to individual attributes or elements of data,such as by assigning a risk score between 0 and 1 to a given attribute(e.g., dryer vent). In other embodiments, risk level unit 440 maydetermine an indication of risk by generating labels which pertain todata in whole or in part. This labeling may be accomplished in variousdifferent ways, depending on the embodiment.

For example, risk level unit 440 may label input data 402, or portionsthereof, according to positive or negative pattern matching according topattern matching unit 428. For example, if input data 402 matches thepattern “dryer vent blocked,” then input data 402 may receive labelssuch as (BLOCKED, VENT, DRYER) or (FIRE, APPLIANCE). Alternately, insome embodiments, risk level unit 440 may label input data 402, whichmay be raw data or a claim filed by a customer, according to resultsobtained from natural language processing unit 430 (e.g., JEWELRY,THEFT). Risk level unit 440 may label input data 402 according toBoolean values (e.g., PAID/NOT-PAID) or pre-determined ranges (e.g.,claims having a payout of $0-$50,000; $50,000-$500,000;$500,000-$1,000,000; or >=$1,000,000).

Labels may be saved to and/or retrieved from an electronic database,such as risk indication data 442, and claim labels may be generated fromalready-existing labels, and/or dynamically created labels (i.e., labelscreated at runtime) by risk level unit 440. A set of labels may beassociated with a set of input data 402, and the creation of new labelsmay be partially or entirely based upon existing labels and/or inputdata 402.

Dynamic creation of labels may, in some embodiments, be based upon userattributes and/or metadata. For example, a resident of the EasternUnited States may be assigned a label related to weather or anotherattribute unique to the region; for example, a hurricane- orflood-related label.

As noted, in some embodiments, risk level unit 440 may analyze inputdata 402 (e.g., label claims) through the use of a neural network unit450. Neural network unit 450 may use an artificial neural network, orsimply “neural network.” The neural network may be any suitable type ofneural network, including, without limitation, a recurrent neuralnetwork or feed-forward neural network. The neural network may includeany number (e.g., thousands) of nodes or “neurons” arranged in multiplelayers, with each neuron processing one or more inputs to generate adecision or other output.

In some embodiments, neural network models (and/or other machinelearning models, modules, algorithms, or programs) may be chainedtogether, so that output from one model is fed into another model asinput. For example, risk level unit 440 may, in one embodiment, applyinput data 402 to a first neural network model (or other machinelearning model, module, algorithm, or program) that is trained togenerate labels. The output (e.g., labels) of this first neural networkmodel (or other machine learning model, module, algorithm, or program)may be fed as input to a second neural network model (or other machinelearning model, module, algorithm, or program) which has been trained topredict, for example, claim settlement amounts based upon the presenceof labels. The second neural network (or other machine learning model,module, algorithm, or program) may be trained using aninflation-adjusted set of claim payout amounts, and respective set ofrisk labels, to very accurately predict the amount of money likely to bepaid on a new claim, given only a new set of risk labels from the firstmodel. In another arrangement, the output of the first neural networkmodel (or other machine learning model, module, algorithm, or program)may be fed as an input to a third neural network model (or other machinelearning model, module, algorithm, or program) which has been trained topredict, for example, a likelihood of damage to a dwelling andrespective repair and/or replacement costs. The third neural network (orother machine learning model, module, algorithm, or program) may betrained based upon insurance claim data and respective sets of risklabels, for example.

Other neural network models may be trained (and optionally chained) topredict other parameters corresponding to and/or attributes ofbuildings, real properties, and/or associated risk and risk mitigationbased upon labeled input data. For example, sets of neural networks maycollectively operate on input data 402 and/or historical claim data 408to predict parameters and/or attributes such as claim risk factors (andoptionally respective measures, levels, or quantifications of risk foreach risk factor); risk mitigation imperatives or actions; claimsettlement amounts; confidence levels, other labels; etc.

Neural network unit 450 may include training unit 452, and riskindication unit 454. To train the neural network to identify risk,neural network unit 450 may access electronic claims within historicaldata 408. Historical data 408 may comprise a corpus of documentsincluding many (e.g., millions) of insurance claims which may containdata linking respective customers or claimants to one or more realproperties, and which may also contain, or be linked to, informationpertaining to the customers. In particular, historical data 408 may beanalyzed by AI platform 404 to generate claim records 410-1 through410-n, where n is any positive integer. Each claim 410-1 through 410-nmay be processed by training unit 452 to train one or more neuralnetworks (or other machine learning model, module, algorithm, orprogram) to identify claim risk factors, including by pre-processing ofhistorical data 408 using input analysis unit 420 as described above,e.g., to generate corresponding labels. For example, the training unit452 may train an artificial neural network (or other artificialintelligence or machine learning algorithm, model, or module) by using asubset of the historical claim data 408 that has respective labelsapplied thereto. The training unit 452 may test and/or validate thetrained network (or the trained, other artificial intelligence ormachine learning algorithm, model, or module) by using anothernon-overlapping subset of the historical claim data 408 (which may ormay not have corresponding labels) to determine the accuracy of the fitof the trained network/algorithm/model/module, and in some cases, toavoid or mitigate over- or under-fitting.

Generally speaking, training an artificial neural network, machinelearning algorithm, model, or module may include establishing a networkarchitecture, or topology, by adding layers such as activation functions(e.g., a rectified linear unit, softmax, etc.), loss function, andoptimizer, to name a few. The data used to train, test, and/or validatethe neural network (e.g., the historical claim data 408) may includerespective data corresponding to a large group of inputs, which may belabeled, and which may be divided into training, validation, and testingdata (e.g., mutually exclusive subsets of the historical claim data408). Data that is input to the neural network (e.g., for training,testing, or validation purposes) may be encoded in an N-dimensionaltensor, array, matrix, or other suitable data structure.

In one embodiment, a different or specific neural network type may beselected or chosen to be trained (e.g., a recurrent neural network, aconvolutional neural network, a deep learning neural network, etc.).Training may be performed by successive evaluation (e.g., looping) ofthe network by using labeled training samples, e.g., subsets of thelabeled historical claim data 408. The process of training theartificial neural network may cause weights or parameters of theartificial neural network to be created. The created weights maycorrespond to, for example, one or more labels, either alone or incombination; static characteristics of buildings and/or real properties,dynamic characteristics of buildings and/or real properties, and/orcombinations thereof; and/or other information, attributes,characteristics, or parameters included in and/or derived from thehistorical claim data. In some implementations, the weights may beinitialized to random values. The weights may be adjusted as the networkis successively trained, e.g., by using one of several gradient descentalgorithms, to reduce loss and to cause the values output by the networkto converge to expected, or “learned,” values.

In one embodiment, a regression neural network, which has no activationfunction, may be selected or chosen. Therein, input data may benormalized by mean centering, and a mean squared error loss function maybe used, in addition to mean absolute error, to determine theappropriate loss as well as to quantify the accuracy of the outputs.

Trained networks, algorithms, models, and/or modules may be subject tovalidation and cross-validation using standard techniques (e.g., byhold-out, K-fold, etc.). In some embodiments, multiple neural networksmay be separately trained and operated.

At any rate, neural network 450 may, from a trained model (or othermachine learning model, module, algorithm, or program), identify labelsthat correspond to specific data, metadata, and/or attributes withininput data 402, depending on the embodiment. For example, neural network450 may be provided with instructions from input analysis unit 420indicating that one or more particular type of insurance is associatedwith one or more portions of input data 402.

Neural network 450 may identify one or more insurance types associatedwith the one or more portions of input data 402 (e.g., dwellingcoverage, personal property or contents coverage, personal liability,earthquake insurance, flood insurance, water back up of sewer, otherstructures insurance, medical payments, etc.) and by input analysis unit420. In one embodiment, the one or more insurance types may beidentified by training the neural network 450 based upon types of peril.For example, the neural network model (or other machine learning model,module, algorithm, or program) may be trained to determine that fire,theft, or vandalism may indicate comprehensive property owner'sinsurance coverage.

In addition, input data 402 may indicate a particular real property. Inthat case, risk level unit 440 may look up additional real propertyinformation from customer data 450 corresponding to the owner of theparticular real property, and real property data 452 corresponding tothe particular real property, respectively. For example, the age and/ortype of the particular real property (e.g., single family home,apartment building, business storefront, etc.) may be obtained. Inanother example, if a customer is a business or corporation that ownsmultiple buildings, customer data 450 may include historical data ofclaims filed by the owner for any of the multiple buildings. Theadditional customer and/or real property information may be provided toneural network unit 450 and may be used to analyze and label input data402 and, ultimately, may be used to determine risk. For example, neuralnetwork unit 450 may be used to predict risk based upon inputs obtainedfrom a party applying for an insurance policy for the real property, orbased upon a claim submitted by a party who is a holder of an existinginsurance policy. That is, in some embodiments where neural network unit450 is trained on claim data, neural network unit 450 may predict riskbased upon raw information unrelated to the claims filing process, orbased upon other data obtained during the filing of a claim (e.g., aclaim record retrieved from historical data 408).

In one embodiment, the training process may be performed in parallel,and training unit 452 may analyze all or a subset of claims 410-1through 410-n. Specifically, training unit 452 may train a neuralnetwork (or other machine learning model, module, algorithm, or program)to identify claim risk factors in claim records 410-1 through 410-n. Asnoted, AI platform 404 may analyze input data 402 to arrange thehistorical claims into claim records 410-1 through 410-n, where n is anypositive integer.

Claim records 410-1 through 410-n may be organized in a flat liststructure, in a hierarchical tree structure, or by means of any othersuitable data structure. For example, the claim records may be arrangedin a tree wherein each branch of the tree is representative of one ormore customer. There, each of claim records 410-1 through 410-n mayrepresent a single non-branching claim, or may represent multiple claimrecords arranged in a group or tree.

Further, claim records 410-1 through 410-n may comprise links tocustomers and real properties whose corresponding data is locatedelsewhere. In this way, one or more claims may be associated with one ormore customers and one or more real properties via one-to-many and/ormany-to-one relationships. Risk factors may be data indicative of aparticular risk or risks associated with a given claim, customer, and/orreal property. The status of claim records may be completely settled orin various stages of settlement.

As used herein, the term “claim” or “real property claim” generallyrefers to an electronic document, record, or file, that represents aninsurance claim (e.g., an insurance claim on a house, home, building, orother type of real property) submitted by a policy holder of aninsurance company. Herein, “claim data” or “historical data” generallyrefers to data directly entered by the customer or insurance companyincluding, without limitation, free-form text notes, photographs, audiorecordings, written records, receipts (e.g., hotel and rental car,purchase of replacement materials, repair labor, etc.), and otherinformation including data from legacy, including pre-Internet (e.g.,paper file), systems. Notes from claim adjusters and attorneys may alsobe included.

In one embodiment, claim data may include claim metadata or externaldata, which generally refers to data pertaining to the claim that may bederived from claim data or which otherwise describes, or is related to,the claim but may not be part of the electronic claim record. Claimmetadata may have been generated directly by a developer of theenvironment 400, for example, or may have been automatically generatedas a direct product or byproduct of a process carried out in environment400. For example, claim metadata may include a field indicating whethera claim was settled or not settled, and amount of any payouts, and theidentity of corresponding payees.

Another example of claim metadata is the geographic location in which aproperty is located. Yet another example of claim metadata includes acategory of the claim type (e.g., damage to the building structure,theft of articles, liability, etc.). For example, a single claim inhistorical data 408 may be associated with a company that owns and/orleases several buildings, and may include the name, address, and otherinformation relating to the company and well as information pertainingto the building portfolio owned/leased by the company.

The claim may include a plurality of claim data and claim metadata,including metadata indicating a relationship or linkage to other claimsin historical claim data 408. In this way, neural network unit 450 mayproduce a neural network that has been trained to associate the presenceof certain input parameters with higher or lower risk levels. A specificexample of a claim is discussed with respect to FIG. 5, below.

Once the neural network (or other machine learning model, module,algorithm, or program) has been trained, risk indication unit 454 mayapply the trained neural network (or other trained machine learningmodel, module, algorithm, or program) to input data 402 as processed byinput analysis unit 420. In one embodiment, input analysis unit 420 maymerely “pass through” input data 402 without modification. The output ofthe neural network (or other machine learning model, module, algorithm,or program), indicating risk indications, such as labels pertaining tothe entirety of, or portions of input data 402, may then be provided torisk level unit 440. Risk level unit 440 may insert the output of theneural network (or other machine learning model, module, algorithm, orprogram) (e.g., labels) into an electronic database, such as riskindication data 442. Alternatively, or additionally, risk indicationunit 454 may use label information output by the neural network (orother machine learning model, module, algorithm, or program) todetermine attributes of input data 402, and may provide those attributesto risk level unit 440.

In some embodiments, each label or attribute may be associated with aconfidence score and/or weight. Confidence scores may be assigned basedupon the source of the information (e.g., if the information is fromreal property data 574, such as telematics data, then a score of 1.0 maybe assigned; whereas, if the information is inferred and/or provided bya user, a lower confidence score may be assigned). Risk level unit 440may then forward the labels and/or scores to risk level analysisplatform 406. In some embodiments, determining a single label mayrequire neural network unit 450 to analyze several attributes withininput data 402. For example, an application for a new homeownersinsurance policy may be required to provide the home's age, type (e.g.,ranch, two-story, split-level, etc.), and geographical location. Somemodels may include validation that will produce an error state if arequired piece of information is not provided.

AI platform 404 may further include customer data 450 and real propertydata 452, which risk level unit 440 may leverage to provide useful inputparameters to neural network unit 450. Customer data 450 may be anintegral part of AI platform 404, or may be located separately from AIplatform 404. In some embodiments, customer data 450 or real propertydata 452 may be provided to AI platform 404 via separate means (e.g.,via an API or Application Programming Interface call), and may beaccessed by other units or components of environment 400. Either may beprovided by a third-party service.

Real property data 452 may include a database comprising informationdescribing various types of real property, including information aboutlegal names or identification of properties, the year a structure wasbuilt, square footage, location, materials used, amount of personalproperty insured, whether or not additional types of insurance such asflood or earthquake insurance was purchased for the property, etc. Realproperty data 452 may indicate whether or not a property is equippedwith various features which may affect risk (e.g., security sensorsand/or systems, automatic sprinkler systems, motion detectors, etc.).

Both of customer data 450 and real property data 452 may be used totrain a neural network model (or other machine learning model, module,algorithm, or program). For example, in an example of a new propertyinsurance application to cover a target property, risk level unit 440may look up the applicant in the customer data 450 to determine thepresence and contents of the applicant's property insurance claimhistory (e.g., for other properties that have been owned by theapplicant), and may obtain from real property data 452 the knowledge ofvarious characteristics of the target property and/or any propertyinsurance claims that were filed by previous owners of the targetproperty.

All of the information pertaining to the applicant may then be providedto neural network unit 450, which may—based upon its prior training onclaims from historical data 408—determine that a plurality of labelsapply to the applicant and/or to the target property. For example, thelabels may include (e.g., FLOODPLAIN, BASEMENT). As noted, the labelsmay have a respective confidence factor, and may be sorted in terms ofcriticality, and/or given pre-assigned weights. The labels and/orweights may be stored in risk indication data 442, in an embodiment. Itshould be appreciated that the use of additional real property labels(e.g., FINISHED-BASEMENT, SUMP PUMP, GENERATOR) is envisioned in labelgeneration.

In some embodiments, pattern matching unit 428 and natural languageprocessing unit 430 may act in conjunction to determine labels. Forexample, pattern matching unit 428 may include instructions to identifywords indicating flooding or the undesired presence of water (e.g.,“leak,” “damp,” “puddle,” “mold”). Matched data may be provided tonatural language processing unit 430, which may further process thematched data to determine parts of speech such as verbs and objects, aswell as relationships between the objects.

The output of natural language processing unit 430 may be provided toneural network unit 450 and used by training unit 452 to train a neuralnetwork model (or other machine learning model, module, algorithm, orprogram) to label insurance types. For example, if natural languageprocessing unit 452 indicates a theft of electronics or other personalproperty, then the neural network (or other machine learning model,module, algorithm, or program) may generate a label of THEFT, indicatingthat the input data 402 may indicate a personal property or personalarticles insurance policy. On the other hand, if natural languageprocessing unit 452 indicates damage to multiple electronics within ahome (e.g., due to a power surge), then the neural network may generatea label of COMPREHENSIVE.

It should be appreciated that in this example, the two labels (THEFT andCOMPREHENSIVE) are not mutually exclusive. That is, the neural networkmodel (or other machine learning model, module, algorithm, or program)may generate multiple labels corresponding to an indication by patternmatching unit 428 and/or natural language processing unit 430 that bothtypes of insurance coverage are indicated. For example, due to a powersurge, electronic locks may be disabled, thus enabling the theft of thepersonal articles. Further, additional processing, including by the useof an additional neural network model (or other machine learning model,module, algorithm, or program), maybe used to assign weight to a label.For example, an injury of a person who slipped and fell on an ice damlocated on the front steps may receive a higher weight than an injury ofa person who tripped over his or her own feet and fell in the middle ofa room.

The labels in risk indication data 442 may be provided to risk levelanalysis platform which may perform a calculation using the labelsand/or weights. For example, in one embodiment, risk level analysisplatform 406 may sum the weights and scale the price of a policy offeredto the applicant. In other embodiments, the risk level analysis platform406 may apply a cut-off level, beyond which no policy may be offered. Inyet another embodiment, a maximum and/or minimum weight may be computed,and used to scale a base price. A maximum or minimum weight maycorrespond to a local maximum (e.g., the deepest or highest level offlood waters measured in a neighborhood), a global maximum (e.g., thehomeowner of a set of homeowners with the most claims filed in afive-year period), or a maximum among a set of property owners.

It should be appreciated that there are many possibilities for using therisk-related information generated by the neural network. For example,when claim data related to a real property is received as input data 402and analyzed using the trained neural network, resulting informationthat is generated by the neural network and associated with identifiedrisk may include one or more labels (which may be the same or differentfrom input labels), one or more mitigation imperatives or actions thatmay be taken to reduce risk at the real property, a claim mitigationplan (which may include, for example, multiple mitigation imperativesaddressing different risk factors), and the like. The resultinginformation may be generated directly by the trained neural network, ormay be generated by one or more other units (e.g., within the risk levelanalysis platform 406) operating on output of a trained neural network(or of a chained set of trained neural networks) that is indicative orrisk types and/or degrees of risk.

In some embodiments, labels may be associated with pre-set weights thatare stored separately from AI platform 404, and which may be updatedindependently. It should also be appreciated that the methods andtechniques described herein may not be applied to seek profit in aninsurance marketplace. Rather, the methods and techniques may be used tomore fairly and equitably allocate risk among customers in a way that isrevenue-neutral, yet which strives for fairness to all marketparticipants, and may only be used on an opt-in basis. For example, ahomeowner may opt-in to having telematics data generated by his or herhome (and/or various appliances, systems, and components therein)automatically utilized to help set an insurance premium that is morereflective of risk to the home.

Historically, claim losses may be categorized using loss cause codes.These may be a handful of mutually-exclusive labels or categories intowhich claims are categorized that only permit coarse analysis of risk.However, the methods and systems described herein may help risk-aversecustomers to lower their insurance premiums by more granularlyquantifying risk. The methods and systems may also allow new customersto receive more accurate pricing when they are shopping for realproperty insurance products. All of the benefits provided by the methodsand systems described herein may be realized much more quickly thantraditional modeling approaches.

Exemplary Training Model System

With reference to FIG. 5, a high-level block diagram of real propertyinsurance risk training model system 500 is illustrated that mayimplement communications between a client device 502 and a server device504 via network 506 to provide real property insurance lossclassification and/or risk level analysis. FIG. 5 may correspond to oneembodiment of the system 100 of FIG. 1 and/or the environment 400 ofFIG. 4, and also may include various user/client-side components. Forsimplicity, client device 502 is referred to herein as client 502, andserver device 504 is referred to herein as server 504, but either devicemay be any suitable computing device (e.g., a laptop, smart phone,tablet, server, wearable device, etc.). Indeed, in one embodiment, theclient 502 may comprise one or more intelligent monitoring systemcontrollers 106, 106R and/or intelligent monitoring system servers 140such as shown in FIG. 1. In some implementations, monitoring systemserver 140 and training model system server 504 may be an integralserver, or may be separate and distinct servers that are communicativelyconnected, e.g., via one or more networks 132. Generally speaking,server 504 may host services relating to neural network training andoperation, and may be communicatively coupled to client 502 via network506.

Although only one client device is depicted in FIG. 5, it should beunderstood that any number of client devices 502 may be supported.Client device 502 may include a memory 508 and a processor 510 forstoring and executing, respectively, a module 512. While referred to inthe singular, processor 510 may include any suitable number ofprocessors of one or more types (e.g., one or more CPUs, graphicsprocessing units (GPUs), cores, etc.). Similarly, memory 508 may includeone or more persistent memories (e.g., a hard drive and/or solid statememory).

Module 512, stored in memory 508 as a set of computer-readableinstructions, may be related to an input data collection application 516which, when executed by the processor 510, causes input data to bestored in memory 508. The data stored in memory 508 may correspond to,for example, raw data retrieved from input data 402. Input datacollection application 516 may be implemented as web page (e.g., HTML,JavaScript, CSS, etc.) and/or as a mobile application for use on astandard mobile computing platform.

Input data collection application 516 may store information in memory508, including the instructions required for its execution. While theuser is using input data collection application 516, scripts and otherinstructions comprising input data collection application 516 may berepresented in memory 508 as a web or mobile application. Additionallyor alternatively, while the client device 502 is automaticallycollecting telematics data generated by one or more real properties,input data collection application 516 may execute, e.g., in thebackground, of the client device 502. The input data collected by inputdata collection application 516 may be stored in memory 508 and/ortransmitted to server device 504 by network interface 514 via network506, where the input data may be processed as described above todetermine a series of risk indications and/or a risk level. In oneembodiment, input data collection application 516 may be data used totrain a model (e.g., scanned claim data).

Client device 502 may also include GPS sensor 518, an image sensor 520,user input device 522 (e.g., a keyboard, mouse, touchpad, and/or otherinput peripheral device), and display interface 524 (e.g., an LEDscreen). User input device 522 may include components that are integralto client device 502, and/or exterior components that arecommunicatively coupled to client device 502, to enable client device502 to accept inputs from the user. Display 524 may be either integralor external to client device 502, and may employ any suitable displaytechnology. In some embodiments, input device 522 and display 524 areintegrated, such as in a touchscreen display. Execution of the module512 may further cause the processor 510 to associate device datacollected from client 502 such as a time, date, and/or sensor data(e.g., a camera for photographic or video data) with real propertyand/or customer data, such as data retrieved from customer data 460 andreal property data 462, respectively.

In some embodiments, client 502 may receive data from risk indicationdata 442 and risk level analysis platform 406. Such data, indicatingrisk labels and/or a risk level computation, may be presented to a userof client 502 by a display interface 524.

Execution of the module 512 may further cause the processor 510 of theclient 502 to communicate with the processor 550 of the server 504 vianetwork interface 514 and network 506. As an example, an applicationrelated to module 512, such as input data collection application 516,may, when executed by processor 510, cause a user interface to bedisplayed to a user of client device 502 via display interface 524. Theapplication may include graphical user input (GUI) components foracquiring data (e.g., photographs) from image sensor 520, GPS coordinatedata from GPS sensor 518, and textual user input from user inputdevice(s) 522.

The processor 510 may transmit the aforementioned acquired data toserver 504, and processor 550 may pass the acquired data to a neuralnetwork (or other machine learning model, module, algorithm, orprogram), which may accept the acquired data and perform a computation(e.g., training of the model, or application of the acquired data to atrained neural network model (or other machine learning model, module,algorithm, or program) to obtain a result). With specific reference toFIG. 5, the data acquired by client 502 may be transmitted via network506 to a server implementing AI platform 404, and may be processed byinput analysis unit 420 before being applied to a trained neural network(or other machine learning model, module, algorithm, or program) by risklevel unit 440.

As described with respect to FIG. 5, the processing of input from client502 may include associating customer data 460 and real property data 462with the acquired data. The output of the neural network (or othermachine learning model, module, algorithm, or program) may betransmitted, by a risk level unit corresponding to risk level unit 440in server 504, back to client 502 for display (e.g., in display 524)and/or for further processing.

Network interface 514 may be configured to facilitate communicationsbetween client 502 and server 504 via any hardwired or wirelesscommunication network, including network 506 which may be a singlecommunication network, or may include multiple communication networks ofone or more types (e.g., one or more wired and/or wireless local areanetworks (LANs), and/or one or more wired and/or wireless wide areanetworks (WANs) such as the Internet). Client 502 may cause insurancerisk related data to be stored in server 504 memory 552 and/or a remoteinsurance related database such as customer data 460.

Server 504 may include a processor 550 and a memory 552 for executingand storing, respectively, a module 554. Module 554, stored in memory552 as a set of computer-readable instructions, may facilitateapplications related to processing and/or collecting insurance riskrelated data, including claim data and claim metadata, and insurancepolicy application data. For example, module 554 may include inputanalysis application 560, risk level application 562, and neural networktraining application 564, in one embodiment.

Input analysis application 560 may correspond to input analysis unit 420of environment 400 of FIG. 4. Risk level application 562 may correspondto risk level unit 440 of environment of FIG. 4, and neural networktraining application 564 may correspond to neural network unit 450 ofenvironment 400 of FIG. 4. Module 554 and the applications containedtherein may include instructions which, when executed by processor 550,cause server 504 to receive and/or retrieve input data from (e.g., rawdata and/or an electronic claim) from client device 502. In oneembodiment, input analysis application 560 may process the data fromclient 502, such as by matching patterns, converting raw text tostructured text via natural language processing, by extracting contentfrom images, by converting speech to text, and so on.

Throughout the aforementioned processing, processor 550 may read datafrom, and write data to, a location of memory 552 and/or to one or moredatabases associated with server 504. For example, instructions includedin module 554 may cause processor 550 to read data from an historicaldata 570, which may include historical property insurance claim dataamong other data stored at a data storage area or system, which may becommunicatively coupled to server device 504, either directly or viacommunication network 506. Historical data 570 may correspond tohistorical data 408, and processor 550 may contain instructionsspecifying analysis of a series of electronic claim documents fromhistorical data 570, as described above with respect to claims 410-1through 410-n of historical data 408 in FIG. 4.

Processor 550 may query customer data 572 and real property data 574 fordata related to respective electronic claim documents and raw data,e.g., as described with respect to FIG. 4. In one embodiment customerdata 572 and real property data 574 correspond, respectively, customerdata 460 and real property 462. In another embodiment, customer data 572and/or real property data 574 may not be integral to server 504. Module554 may also facilitate communication between client 502 and server 504via network interface 556 and network 506, in addition to otherinstructions and functions.

Although only a single server 504 is depicted in FIG. 5, it should beappreciated that it may be advantageous in some embodiments to provisionmultiple servers for the deployment and functioning of AI system 402.For example, the pattern matching unit 428 and natural languageprocessing unit 430 of input analysis unit 420 may require CPU-intensiveprocessing. Therefore, deploying additional hardware may provideadditional execution speed. Each of historical data 570, customer data572, real property data 574, and risk indication data 576 may begeographically distributed. For example, at least a portion of theserver 504 may be implemented using a cloud computing system or othersuitable distributed processing system.

While the databases depicted in FIG. 5 are shown as beingcommunicatively coupled to server 504, it should be understood thathistorical claim data 570, for example, may be located within separateremote servers or any other suitable computing devices communicativelycoupled to server 504. For example, at least a portion of the historicalclaim data 570 may be stored using a cloud data storage system or othersuitable distributed data storage system. As such, distributed databasetechniques (e.g., sharding and/or partitioning) may be used todistribute data. In one embodiment, a free or open source softwareframework such as Apache Hadoop® may be used to distribute data and runapplications (e.g., risk level application 562). It should also beappreciated that different security needs, including those mandated bylaws and government regulations, may in some cases affect the embodimentchosen, and configuration of services and components.

In a manner similar to that discussed above in connection with FIG. 4,historical claims from historical claim data 570 may be ingested byserver 504 and used by neural network training application 564 to trainan artificial neural network (or other machine learning model, module,algorithm, or program). Then, when module 554 processes input fromclient 502, the data output by the neural network(s) (or other machinelearning models, modules, algorithms, or programs) (e.g., dataindicating labels, risks, weights, etc.) may be passed to risk levelapplication 562 for computation of an overall risk level, which asdiscussed, may be expressed in Boolean, decimal, or any other suitableformat. The calculated risk level may then be transmitted to clientdevice 502 and/or another device. The calculated risk level may be usedfor further processing by client device 502, server device 504, oranother device.

It should be appreciated that the client/server configuration depictedand described with respect to FIG. 5 is but one possible embodiment. Insome cases, a client device such as client 502 may not be used. In thatcase, input data may be entered—programmatically, or manually—directlyinto device 504. A computer program or human may perform such dataentry. In that case, device may contain additional or fewer components,including input device(s) and/or display device(s).

The most useful embodiment may vary according to the purpose for whichthe AI platform is being utilized—for example, a different hardwareconfiguration may be preferable if the AI platform is being used toprovide a risk analysis to an end user or customer, whereas anotherembodiment may be preferable if the AI platform is being used to providerisk as part of a backend service. Furthermore, it may be possible topackage the trained neural network (or other machine learning model,module, algorithm, or program) for distribution to a client 502 (i.e.,the trained neural network may be operated on the client 502 without theuse of a server 504).

In operation, the user of client device 502, by operating input device522 and viewing display 524, may open input data collection application516, which depending on the embodiment, may allow the user to enterpersonal information. The user may be an employee of a companycontrolling AI platform 404 or a customer or end user of the company.For example, input data collection application 516 may walk the userthrough the steps of submitting a claim.

Before the user can fully access input data collection application 516,the user may be required to authenticate (e.g., enter a valid usernameand password). The user may then utilize input data collectionapplication 516. Module 512 may contain instructions that identify theuser and cause input data collection application 516 to present aparticular set of questions or prompts for input to the user, based uponany information input data collection application 516 collects,including without limitation information about the user or any realproperty.

Further, module 512 may identify a subset of historical data 570 to beused in training a neural network (or other machine learning model,module, algorithm, or program), and/or may indicate to server device 504that the use of a particular neural network model or models (or othermachine learning model, module, algorithm, or program) is appropriate.For example, if the user is applying for earthquake insurance for aparticular building, then module 512 may transmit the user's name andpersonal information, the location of the building to be insured, aphotograph of the building to be insured (which may be captured by imagesensor 520); and information indicative of building materials andtechniques used to construct the building to server device 504.

In some embodiments, location data from client device 502 may be used bya neural network (or other machine learning model, module, algorithm, orprogram) to label risk, and labels may be linked, in that a first labelimplies a second label. As noted above, location may be provided to oneor more neural networks (or other machine learning models, modules,algorithms, or programs) in the AI platform to generate labels anddetermine risk. For example, the zip code of a piece of property,whether provided via GPS or entered manually by a user, may cause theneural network (or other machine learning model, module, algorithm, orprogram) to generate a label applicable to the property such as RURAL,SUBURBAN, or URBAN.

Such qualifications may be used in the calculation of risk, and may beweighted accordingly. For example, the neural network may assign ahigher risk weight to the URBAN label, due to the increased likelihoodof theft of personal property. Due to the increased risk of theft ofpersonal property, the generation of an URBAN label may be accompaniedby additional labels such as THEFT. Alternatively, or in addition, thepersonal property theft label weight may be increased along with theaddition of the URBAN label.

Another label, such as LIGHTNING, may be associated with buildings whichthe neural network labels as (RURAL, PLAINS). In some embodiments, labelgeneration may be based upon seasonal information, in whole or in part.Additionally or alternatively, the neural network (or other machinelearning model, module, algorithm, or program) may generate labels,and/or adjust label weights based upon location provided in input data.For example, the trained neural network model (or other machine learningmodel, module, algorithm, or program) may learn to associate buildingslocated on the eastern seaboard of the United States with higher riskduring hurricane season.

All other inputs being equal, real property risk may differ based uponthe time of year when an applicant is applying for real propertyinsurance. Indeed, using the techniques described herein, risk of aparticular real property may vary throughout a calendar year (e.g.,based upon seasons and/or weather), and the varying levels of risk maybe reflected in varying premium amounts, which may be adjustedthroughout the calendar year. It should be appreciated that the quickand automatic generation of such associations is a benefit of themethods and systems disclosed herein, and that some of the associationsmay appear counter-intuitive when analyzing large data sets.

By the time the user of client 502 submits an application for realproperty insurance or files a claim, server 504 may have alreadyprocessed the electronic claim records in historical data 570 andtrained a neural network model (or other machine learning model, module,algorithm, or program) to analyze the information provided by the userto output risk indications, labels, and/or weights.

For example, a homeowner may access client 502 to submit a claim underthe homeowner's insurance policy related to damage to the home's kitchendue to a cooking fire. Client 502 may collect information from thehomeowner related to the circumstances of the cooking fire in additionto demographic information of the home (e.g., smoke detectors,auto-shut-off of appliances, sprinkler system, etc.), such asphotographs from image sensor 520, dynamic telematics data provided bythe home's monitoring system in the period of time during which thecooking fire occurred, historical telematics data over time, etc. Insome embodiments, the homeowner may be prompted to make a telephone callto discuss the filing of the claim, which may be recorded and laterprovided to server 504. Additionally or alternatively, a reportgenerated by the fire department that put out the fire and/orcorresponding 911 call records may be obtained and provided to theserver 504.

All of the information collected may be associated with a claimidentification number so that it may be referenced as a whole. Server504 may process the information as it arrives, and thus may processinformation collected by input data collection application 516 at adifferent time than server 504 processes the audio recording, thecurrent and historical home telematics data, the fire department report,and the 911 call records in the above example. Once informationsufficient to process the claim has been collected, server 504 may passall of the processed information (e.g., from input analysis application)to risk level application 562, which may apply the information to thetrained neural network model (or other machine learning model, module,algorithm, or program).

While the claim or application processing is pending, client device 502may display an indication that the processing of the claim is ongoingand/or incomplete. When the claim is ultimately processed by server 504,an indication of completeness may be transmitted to client 502 anddisplayed to user, for example via display 524. Missing information maycause the model to abort with an error.

In some embodiments, the labels and/or characterization of input data(claims and otherwise) performed by the systems and methods describedherein may be capable of dynamic, incremental, and or online training.Specifically, a model that has been trained on a set of electronic claimrecords from historical data 570 may be updated dynamically, such thatthe model may be updated on a much shorter time scale. For example, themodel may be adjusted weekly or monthly to take into accountnewly-settled claims.

In one embodiment, the settlement of a claim may trigger an immediateupdate of one or more neural network models (or other machine learningmodel, module, algorithm, or program) included in the AI platform. Forexample, the settlement of a claim involving roof and gutter repair dueto the weight of ice and snow may trigger updates to a set of neuralnetwork models (or other machine learning models, modules, algorithms,or programs) pertaining to coverage due to ice and snow weight for aparticular geographical regions. In addition, or alternatively, as newclaims are filed and processed, new labels may be dynamically generated,based upon risks identified and generated during the training process.In some embodiments, a human reviewer or team of reviewers may beresponsible for approving the generated labels and any associatedweightings before they are used.

In some embodiments, AI platform 404 may be trained and/or updated toprovide one or more dynamic insurance rating models which may beprovided to, for example, a governmental agency. As discussed above,models are historically difficult to update and updates may be performedon a yearly basis. Using the techniques described herein, models may bedynamically updated in real-time, or on a shorter schedule (e.g.,weekly) based upon new claim data.

While FIG. 5 depicts a particular embodiment, the various components ofenvironment 500 may interoperate in a manner that is different from thatdescribed above, and/or the environment 500 may include additionalcomponents not shown in FIG. 5. For example, an additionalserver/platform may act as an interface between client device 502 andserver device 504, and may perform various operations associated withproviding the labeling and/or risk analysis operations of server 504 toclient device 502 and/or other servers.

Exemplary Artificial Neural Network

FIG. 6 depicts an exemplary artificial neural network 600 which may betrained by neural network unit 450 of FIG. 4 or neural network trainingapplication 564 of FIG. 5, according to one embodiment and scenario. Theexample neural network 600 may include layers of neurons, includinginput layer 602, one or more hidden layers 604-1 through 604-n, andoutput layer 606. Each layer comprising neural network 600 may includeany number of neurons—i.e., q and r may be any positive integers. Itshould be understood that neural networks may be used to achieve themethods and systems described herein that are of a different structureand configuration than those depicted in FIG. 6.

Input layer 602 may receive different input data. For example, inputlayer 602 may include a first input a₁ which represents an insurancetype for property (e.g., dwelling), a second input a₂ representingpatterns identified in input data, a third input a₃ representing a typeof dwelling or building, a fourth input a₄ representing one or materialsfrom which the dwelling or building is constructed, a fifth input a₅representing whether a claim was paid or not paid, a sixth input a₆representing an inflation-adjusted dollar amount disbursed under aclaim, and so on. Input layer 602 may comprise thousands or more inputs.In some embodiments, the number of elements used by neural network 600may change during the training process, and some neurons may be bypassedor ignored if, for example, during execution of the neural network, theyare determined to be of less relevance.

Each neuron in hidden layer(s) 604-1 through 604-n may process one ormore inputs from input layer 602, and/or one or more outputs from aprevious one of the hidden layers, to generate a decision or otheroutput. Output layer 606 may include one or more outputs each indicatinga label, confidence factor, and/or weight describing one or more inputs.A label may indicate the presence (ROOF, HAIL) or absence (DROUGHT) of acondition. In some embodiments, however, outputs of neural network 600may be obtained from a hidden layer 604-1 through 604-n in addition to,or in place of, output(s) from output layer(s) 606.

In some embodiments, each layer may have a discrete, recognizable,function with respect to input data. For example, if n=3, a first layermay analyze one dimension of inputs, a second layer a second dimension,and the final layer a third dimension of the inputs, where alldimensions are analyzing a distinct and unrelated aspect of the inputdata. For example, the first dimension may correspond to aspects of areal property that are considered as strongly determinative, then thesecond dimension may correspond to those that are considered ofintermediate importance, and finally the third dimension may correspondto those that are of less relevance.

In other embodiments, the layers may not be clearly delineated in termsof the functionality they respectively perform. For example, two or moreof hidden layers 604-1 through 604-n may share decisions relating tolabeling, with no single layer making an independent decision as tolabeling.

In some embodiments, neural network 600 may be constituted by arecurrent neural network, wherein the calculation performed at eachneuron is dependent upon a previous calculation. It should beappreciated that recurrent neural networks may be more useful inperforming certain tasks, such as automatic labeling of images.Therefore, in one embodiment, a recurrent neural network may be trainedwith respect to a specific piece of functionality with respect toenvironment 400 of FIG. 4. For example, in one embodiment, a recurrentneural network may be trained and utilized as part of image processingunit 424 to automatically label images.

FIG. 7 depicts an example neuron 700 that may correspond to the neuronlabeled as “1,1” in hidden layer 604-1 of FIG. 6, according to oneembodiment. Each of the inputs to neuron 700 (e.g., the inputscomprising input layer 602) may be weighted, such that input a₁ througha_(p) corresponds to weights w₁ through w_(p), as determined during thetraining process of neural network 600.

In some embodiments, some inputs may lack an explicit weight, or may beassociated with a weight below a relevant threshold. The weights may beapplied to a function α 710, which may be a summation and may produce avalue z₁ which may be input to a function 720, labeled as f_(1,1)(z₁).The function 720 may be any suitable linear or non-linear, or sigmoid,function. As depicted in FIG. 7, the function 720 may produce multipleoutputs, which may be provided to neuron(s) of a subsequent layer, orused directly as an output of neural network 600. For example, theoutputs may correspond to index values in a dictionary of labels, or maybe calculated values used as inputs to subsequent functions.

It should be appreciated that the structure and function of the neuralnetwork 600 and neuron 700 depicted are for illustration purposes only,and that other suitable configurations may exist. For example, theoutput of any given neuron may depend not only on values determined bypast neurons, but also future neurons.

Exemplary Processing of a Claim

The specific manner in which the one or more neural networks employmachine learning to label and/or quantify risk may differ depending onthe content and arrangement of training documents within the historicaldata (e.g., historical data 408 of FIG. 4 and historical data 570 ofFIG. 5) and the input data provided by customers or users of the AIplatform (e.g., input data 402 of FIG. 4 and the data collected by inputdata collection application 516 of FIG. 5), as well as the data that isjoined to the historical data and input data, such as customer data 450of FIG. 4 and customer data 572 of FIG. 5, and real property data 462 ofFIG. 4 and real property data 574 of FIG. 5.

The initial structure of the neural networks (e.g., the number of neuralnetworks, their respective types, number of layers, and neurons perlayer, etc.) may also affect the manner in which the trained neuralnetwork processes the input and claims. Also, as noted above, the outputproduced by neural networks may be counter-intuitive and very complex.For illustrative purposes, intuitive and simplified examples will now bediscussed in connection with FIG. 8.

FIG. 8 depicts text-based content of an example electronic claim record800 which may be processed using an artificial neural network, such asneural network 600 of FIG. 6 or a different neural network generated byneural network unit 450 of FIG. 4 or neural network training application564 of FIG. 5. The term “text-based content” as used herein includesprinting (e.g., characters A-Z and numerals 0-9), in addition tonon-printing characters (e.g., whitespace, line breaks, formatting, andcontrol characters). Text-based content may be in any suitable characterencoding, such as ASCII or UTF-8 and text-based content may includeHTML.

Although text-based-content is depicted in the embodiment of FIG. 8, asdiscussed above, claim input data may include images, includinghand-written notes, and the AI platform may include a neural network (orother machine learning model, module, algorithm, or program) trained torecognize hand-writing and to convert hand-writing to text. Further,“text-based content” may be formatted in any acceptable data format,including structured query language (SQL) tables, flat files,hierarchical data formats (e.g., WL, JSON, etc.) or as other suitableelectronic objects. In some embodiments, image and audio data may be feddirectly into the neural network(s) without being converted to textfirst.

With respect to FIG. 8, electronic claim record 800 includes threesections 810 a-810 c, which respectively represent policy information,loss information, and external information. Policy information 810 a mayinclude information about the insurance policy under which the claim hasbeen made, including the person to whom the policy is issued, theaddress of the insured property, the different types of propertycoverages (e.g., dwelling, contents, liability, etc.). Policyinformation 810 a may be read, for example by input analysis unit 420analyzing historical data such as historical data 408 and individualclaims, such as claims 410-1 through 410-n.

Additional information about the insured property (e.g., location, typeof property, year of construction, square footage, building materials,historical claim data, historical telematics data, etc.) may be obtainedfrom data sources and joined to input data. For example, additionalcustomer data may be obtained from customer data 450 and/or customerdata 572, and additional real property data may be obtained from realproperty data 452 and/or real property data 574. In some embodiments, atleast in addition to policy information 810 a, electronic claim record800 may include loss information 810 b. Loss information generallycorresponds to information regarding a loss event in which a realproperty covered by the policy listed in policy information 810 asustained loss, and may be due to an accident, weather conditions,failure of building component (such as a pipe or electrical circuit),theft, fire, or other peril. Loss information 810 b may indicate thedate and time of the loss, the type of loss (e.g., damage, total loss,theft, etc.), whether personal injury occurred, whether the insured madea statement in connection with the loss, whether the loss was settled,and if so for how much money. Some real property information may beincluded in electronic claim record 800, and the additional lookup maybe of real property attributes (e.g., building materials, squarefootage, etc.).

In some embodiments, more the than one loss may be represented in lossinformation 810 b. For example, a single event may give rise to multiplelosses under a given policy, for example, when a tree on the propertyfalls and damages a part of the building as well as a visitor'sautomobile parked on the property. In addition to loss information,electronic claim record 800 may include external information 810 c,including but not limited to correspondence with the homeowner,statements made by the visitor, before and after photographs or images,etc. External information 810 c may be textual, audio, or videoinformation. The information may include file name references, or may befile handles or addresses that represent links to other files or datasources, such as linked data 820 a-g. It should be appreciated thatalthough only links 820 a-g are shown, more or fewer links may beincluded, in some embodiments.

Electronic claim record 800 may include links to other records,including other electronic claim records. For example, electronic claimrecord 800 may link to notice of loss 820 a, one or more photographs 820b, one or more audio recordings 820 c, one or more investigator'sreports 820 d, one or more forensic reports 820 e, one or more diagrams820 f, and one or more payments 820 g. Data in links 820 a-820 g may beingested by an AI platform such as AI platform 420. For example, asdescribed above, each claim may be ingested and analyzed by inputanalysis unit 420.

AI platform 404 may include instructions which cause input analysis unit420 to retrieve, for each link 820 a-820 g, all available data or asubset thereof. Each link may be processed according to the type of datacontained therein; for example, with respect to FIG. 4, input analysisunit 420 may process, first, all images from one or more photograph 820b using image processing unit 424. Input analysis unit 420 may processaudio recording 820 c using speech-to-text unit 422.

In some embodiments, a relevance order may be established, andprocessing may be completed according to that order. For example,portions of a claim that are identified as most dispositive of risk maybe identified and processed first. If, in that example, they aredispositive of pricing, then processing of further claim elements may beabated to save processing resources. In one embodiment, once a givennumber of labels is generated (e.g., 50) processing may automaticallyabate.

Once the various input data comprising electronic claim record 800 hasbeen processed, the results of the processing may, in one embodiment, bepassed to a text analysis unit, and then to neural network (or othermachine learning model, module, algorithm, or program). If the AIplatform is being trained, then the output of input analysis unit 420may be passed directly to neural network unit 450. The neuronscomprising a first input layer of the neural network being trained byneural network unit 450 may be configured so that each neuron receivesparticular input(s) which may correspond, in one embodiment, to one ormore pieces of information from policy information 810 a, lossinformation 810 b, and external information 810 c.

Similarly, one or more input neurons may be configured to receiveparticular input(s) from links 820 a-820 g. If the AI platform is beingused to accept input to predict a claim value during the claims filingprocess, or to estimate the risk posed by a new customer during theapplication process, then the processing may begin with the use of aninput collection application, as discussed with respect to oneembodiment in FIG. 8.

In some embodiments, analysis of input entered by a user may beperformed on a client device, such as client device 502. In that case,output from input analysis may be transmitted to a server, such asserver 504, and may be passed directly as input to neurons of analready-trained neural network, such as a neural network trained byneural network training application 564.

In one embodiment, the value of a new claim may be predicted directly bya neural network model (or other machine learning model, module,algorithm, or program) trained on historical data 408, without the useof any labeling. For example, a neural network (or other machinelearning model, module, algorithm, or program) may be trained such thatinput parameters correspond to, for example, policy information 810 a,loss information 812 b, external information 812 c, and linkedinformation 820 a-820 g.

The trained model may be configured so that inputting sample parameters,such as those in the example electronic claim record 800, may accuratelypredict, for example, the estimate of damage ($95,000) and settledamount ($94,500). In this case, random weights may be chosen for allinput parameters.

The model may then be provided with training data from claims 410-1through 410-n, which are each pre-processed by the techniques describedherein with respect to FIGS. 4 and 5 to extract individual inputparameters. The electronic claim record 800 may then be tested againstthe model, and the model trained with new training data claims, untilthe predicted dollar values and the correct dollar values converge.

In one embodiment, the AI platform may modify the information availablewithin an electronic claim record. For example, the AI platform maypredict a series of labels as described above that pertain to a givenclaim. The labels may be saved in a risk indication data store, such asrisk indication data 442 with respect to FIG. 4. Next, the labels andcorresponding weights, in one embodiment, may be received by risk levelanalysis platform 406, where they may be used in conjunction with baserate information to predict a claim loss value.

In some embodiments, information pertaining to the claim, such as thecoverage amount and real property type from policy information 810 a,may be passed along with the labels and weights to risk analysisplatform 406 and may be used in the computation of a claim loss value.After the claim loss value is computed, it may be associated with theclaim, for example by writing the amount to the loss information sectionof the electronic claim record (e.g., to the loss information section810 b of FIG. 8).

As noted above, the methods and systems described herein may be capableof analyzing decades of electronic claim records to build neural networkmodels or train other machine learning models, and the formatting ofelectronic claim records may change significantly from decade to decade,even year to year. Therefore, it is important to recognize that theflexibility built into the methods and systems described herein allowselectronic claim records in disparate formats to be consumed andanalyzed.

Exemplary Computer-Implemented Methods

Turning to FIG. 9, an exemplary computer-implemented method 900 fordetermining a risk level posed by a particular real property isdepicted. The method 900 may include training a neural network toidentify risk factors within electronic insurance claim recordscorresponding to the particular real property and/or to the owners ofthe particular real property (e.g., by an AI platform such as AIplatform 404 training a neural network (or other machine learning model,module, algorithm, or program) by an input analysis unit 420 processingdata before passing the results of the analysis to a training unit 452that uses the results to train a neural network model (or other machinelearning model, module, algorithm, or program)) (block 910). The method900 may include receiving information corresponding to the particularreal property by an AI platform (e.g., the AI platform 404 may acceptinput data such as input data 402 and may process that input by the useof an input analysis unit such as input analysis unit 420) (block 920).The method 900 may include analyzing the information using the trainedneural network (e.g., a risk indication unit 454 applies the output ofthe input analysis unit 420 to trained neural network model (or othermachine learning model, module, algorithm, or program)) to generate oneor more risk indicators corresponding to the information (e.g., theneural network produces a plurality of labels and/or correspondingweights) (block 930) which are used to determine a risk levelcorresponding to the particular real property based upon the one or morerisk indicators (e.g., risk indications are stored in risk indicationdata 442, and/or passed to risk level analysis platform 406 forcomputation of a risk level, which may be based upon weights alsogenerated by the trained neural network (or other machine learningmodel, module, algorithm, or program)) (block 940). The method mayinclude additional, less, or alternate actions, including thosediscussed elsewhere herein.

Turning to FIG. 10, a flow diagram for an exemplary computer-implementedmethod 1000 of determining risk indicators from real propertyinformation. The method 1000 may be implemented by a processor (e.g.,processor 550) executing, for example, a portion of AI platform 404,including input analysis unit 420, pattern matching unit 428, naturallanguage processing unit 130, and neural network unit 150. Inparticular, the processor 520 may execute an input data collectionapplication 516 and an input device 522 to cause the processor 525 toacquire application input 1010 from a user of a client 502 and/orautomatically from the client 502 (such as when the client 502 isincluded in an intelligent building monitoring system 100).

The processor 510 may further execute the input data collectionapplication 516 to cause the processor 510 to transmit application input1010 from the user via network interface 514 and a network 506 to aserver (e.g., server 504). Processor 550 of server 504 may cause module554 of server 504 to process application input 1010. Input analysisapplication 560 may analyze application input 1010 according to themethods describe above. For example, real property information may bequeried from real property data such as real property data 574. Anaddress or other geographical indication of the real property inapplication input 1010 may be provided as a parameter to real propertydata 574.

Real property data 574 may return a result indicating that acorresponding real property was found in real property data 574, andthat it is a vacation rental home located in on the Eastern seaboard ofthe United States. Similarly, the purpose provided in application input1010 may be provided to a natural language processing unit (e.g., NLPunit 130), which may return a structured result indicating that the realproperty is owned by a company that owns and rents out multiple vacationrental homes in the area. The result of processing the application input1010 may be provided to a risk level unit (e.g., risk level unit 140)which will apply the input parameters to a trained neural network model(or other trained machine learning model, module, algorithm, orprogram).

In one embodiment, the trained neural network model (or other machinelearning model, module, algorithm, or program) may produce a set oflabels and confidence factors 1020. The set of labels and confidencefactors 1020 may contain labels that are inherent in the applicationinput 1010 (e.g., RENTAL-PROPERTY) or that are queried based uponinformation provided in the application input 1010 (e.g., BEACHFRONT,based upon address). However, the set of labels and confidence factors1020 may include additional labels (e.g., HURRICANE SHUTTERS and RAISEDSTRUCTURE) that are not evident from the application input 1010 or anyrelated/queried information. After being generated by the neural network(or other machine learning model, module, algorithm, or program), theset of labels and confidence factors 1020 may then be saved to anelectronic database such as risk indication data 576, and/or passed to arisk level analysis platform 106, whereupon a total risk may be computedand used in a pricing quote provided to the user of client 502.

It should be appreciated that many more types of information may beextracted from the application input 1010 (e.g., from example links 520a-520 g as shown in FIG. 8). In one embodiment, the pricing quote may bea weighted average of the products of label weights and confidences. Themethod 1000 may be implemented, for example, in response to a partyaccessing client 502 for the purpose of applying for an insurancepolicy, or adding (via an application) an additional insured to anexisting policy. The method may include additional, less, or alternateactions, including those discussed elsewhere herein.

FIG. 11 depicts a flow diagram of an exemplary computer-implementedmethod 1030 of detecting and/or estimating damage to real property. Inone embodiment, one or more processors, servers, sensors, and/ortransceivers are configured to perform at least a portion of the method1030. For example, at least a portion of the method 1030 may beperformed by one or more components of the system 100, the system 400,and/or the system 500. Additionally or alternatively, in someimplementations, the method 1030 may operate in conjunction with one ormore portions of one or more other methods described elsewhere herein.

At any rate, at a block 1032, the method 1030 may include receiving freeform text, voice, and/or speech associated with a submitted insuranceclaim for a damaged insured asset, where the damaged insured assetcomprises a building, home, or another type of real property. Forexample, one or more processors and/or associated transceivers (such asvia wired communication or data transmission, and/or via wirelesscommunication or data transmission over one or more radio links orcommunication channels) may receive the free form text, voice, and/orspeech. The free-form text or voice/speech may be associated with orinput via webpage accessed by customer or by an insurance agent, forexample, or via an Internet page accessed by call center representative.

Additionally, at a block 1035, the method 1030 may include identifying,e.g., via the one or more processors, one or more key words within thefree form text or voice/speech. The one or more key words may be or maybe associated with, for example, fire, smoke, wind, hail, water, stormsurge, tornado, hurricane, electrical, plumbing, property damage,liability, medical, ambulance, materials, cabinets, fireplace, bathroom,bedroom, kitchen, upstairs, roof, downstairs, basement, structure orstructural components, security system, appliance, refrigerator, washer,dryer, oven, stove, and/or lightning, to name a few. In one embodiment,the free-form text or voice/speech may be input into a processor thathas and/or executes a first machine learning algorithm that is trainedto accept, example, at least one type of free from text or voice/speechand/or an indication of at least one type of insured asset, and toidentify at least one keyword associated with the previous word at leastone respective cause of loss and/or peril based upon the accepted input.The first machine learning algorithm may be dynamically or continuouslyupdated or trained to dynamically update a set of keywords associatedwith at least one respective cause of loss and/or peril, if desired.

At a block 1038, the method 1030 may include determining, e.g., via theone or more processors, a cause of loss and/or peril that caused damageto the damaged insured asset to facilitate handling an insurance claimand enhancing online customer experience. The determination may be madeat the block 1038 based upon the one or more keywords, for example, andthe cause of loss and/or peril may be wind, water, storm surge, smoke,fire, hail, hurricane, tornado, etc. In one embodiment, the block 1038may include inputting the one or more keywords into a processor having asecond machine learning algorithm that is trained to accept, as input,at least one keyword and/or an indication of at least one type ofinsured asset, and to identify at least one respective cause of lossand/or peril based upon the accepted input. In some scenarios, thesecond machine learning algorithm may be dynamically or continuouslyupdated or trained to dynamically update a set of causes of loss and/orperils.

Further, in some implementations (not shown in FIG. 11), the method 1030may additionally include retrieving or receiving, e.g., via the one ormore processors and/or transceivers, an insurance policy associated withthe damaged insured asset, and/or determining whether or not thedetermined cause of loss and/or peril is covered under the insurancepolicy. Still further, in some implementations (also not shown in FIG.11), the method 1030 may include receiving, e.g., via the one or moreprocessors and/or transceivers, one or more images of the damagedinsured asset (such digital or electronic images acquired via a mobiledevice or smart home controller), analyzing the one or more images todetermine a second cause of loss and/or peril, and comparing the secondcause of loss or peril with the first determined cause of loss and/or toverify an accuracy of the submitted insurance claim or to identifypotential fraud or build up. For example, at least some of the receivedimages may be input into a machine learning algorithm trained to acceptimages of assets as input and determine a cause of loss and/or periland/or to generate damage estimates and/or repair/replacement costs forthe asset based upon the accepted images.

FIG. 12 depicts a flow diagram of a computer-implemented method 1040 ofdetermining damage to property. In one embodiment, one or moreprocessors, servers, sensors, and/or transceivers are configured toperform at least a portion of the method 1040. For example, at least aportion of the method 1040 may be performed by one or more components ofthe system 100, the system 400, and/or the system 500. Additionally oralternatively, in some implementations, the method 1040 may operate inconjunction with one or more portions of one or more other methodsdescribed elsewhere herein.

The method 1040 may include inputting (block 1042), e.g., via one ormore processors, historical property insurance claim data into a machinelearning algorithm to train the algorithm to identify one or moreinsured assets (and/or respective types thereof), one or more respectiveinsured asset features or characteristics, one or more perils associatedwith the one or more insured assets, and/or respective repair orreplacement costs of at least a portion of the one or more insuredassets. The one or more insured assets may include one or more buildingsand/or types of real property, for example, a house or a home, and theone or more features or characteristics of the damaged insured asset mayinclude location, square footage, cabinet type, roof type, siding type,type of fireplace, and/or material type, to name a few. At a block 1045,the method 1040 may include receiving one or more images, such as one ormore digital images acquired via a mobile device or smartphone or asmart home controller, of a damaged insured asset that is or includesreal property (such as images submitted by the insured via a webpage).The one or more images of the damaged insured asset may be received(block 1045) via the one or more processors and/or one or moretransceivers (such as via wired communication or data transmission,and/or via wireless communication or data transmission over one or moreradio links or communication channels), for example. Additionally, themethod 1040 may include inputting (block 1048), e.g., via one or moreprocessors, the images of the damaged insured asset into a processorhaving or having access to the trained machine learning algorithminstalled in a memory unit. The trained machine learning algorithm may,based upon the input, determine a type of the damaged insured asset, oneor more features or characteristics of the damaged insured asset, aperil associated with the damaged insured asset, and/or a repair orreplacement cost of at least a portion of the damaged insured asset tofacilitate handling an insurance claim associated with the damagedinsured asset. The peril associated with the damaged insured asset maybe at least one of fire, smoke, water, hail, wind, storm surge,hurricane, or tornado.

Further, in some implementations (not shown in FIG. 12), the method 1040may additionally include retrieving or receiving, e.g., via the one ormore processors and/or transceivers, an insurance policy associated withthe damaged insured asset, and/or determining whether or not thedetermined cause of loss and/or peril is covered under the insurancepolicy.

FIG. 13 depicts a flow diagram of a computer-implemented method 1050 fordetermining damage to real property. In one embodiment, one or moreprocessors, servers, sensors, and/or transceivers are configured toperform at least a portion of the method 1050. For example, at least aportion of the method 1050 may be performed by one or more components ofthe system 100, the system 400, and/or the system 500. Additionally oralternatively, in some implementations, the method 1050 may operate inconjunction with one or more portions of one or more other methodsdescribed elsewhere herein.

At a block 1052, the method 1050 may include inputting historical claimdata into a machine learning algorithm to train the algorithm to developa risk profile for an insurable asset based upon a type of the insurableasset and at least one feature or characteristic of the insurable asset,where the insurable asset comprises real property, such as a house orhome. The at least one feature characteristic of the insurable asset mayinclude, for example, at least one of location, square footage, cabinettype, roof type, siding type, type of fireplace, type of windows, ormaterial type, to name a few. At a block 1055, the method 1050 mayfurther include receiving (such as via wired communication or datatransmission, and/or via wireless communication or data transmissionover one or more radio links or communication channels), one or moreimages, such as a digital image acquired via a mobile device or smarthome controller, of an undamaged insurable asset (such as one or moreimages submitted by an insured party via a webpage, website, mobiledevice, and/or smart home controller). Additionally, at a block 1058,the method 1050 may include inputting the one or more images of theundamaged insurable asset into a processor having the trained machinelearning algorithm installed in a memory unit (block 1058). The trainedmachine learning algorithm may, based upon the one or more images,identify or determine a risk profile for the undamaged insurable assetto facilitate generating an insurance quote for the undamaged insurableasset.

In one embodiment (not shown in FIG. 13), the method 1050 may includegenerating an insurance policy and/or determining an insurance rate forthe undamaged insurable asset based at least in part upon the riskprofile developed for the undamaged insurable asset. For example, theinsurance rate may include a usage-based insurance (UBI) rate. Theinsurance policy and/or the insurance rate may be electronicallytransmitted to an owner of the undamaged insurable asset for reviewand/or approval, which may be provided by the owner electronically, ifdesired.

FIG. 14 depicts a flow diagram of an example computer-implemented method1060 for determining damage to real property. In one embodiment, one ormore processors, servers, sensors, and/or transceivers are configured toperform at least a portion of the method 1060. For example, at least aportion of the method 1060 may be performed by one or more components ofthe system 100, the system 400, and/or the system 500. Additionally oralternatively, in some implementations, the method 1060 may operate inconjunction with one or more portions of one or more other methodsdescribed elsewhere herein.

At a block 1062, the method 1060 may include inputting, e.g., via theone or more processors, historical claim data into a machine learningalgorithm to train the algorithm to develop respective risk profiles forat least one insurable asset based upon a type of the at least oneinsurable asset and at least one feature or characteristic of the atleast one insurable asset, where the at least one insurable assetcomprises real property, such as a house or a home. The at least onefeature characteristic of the insurable asset may include, for example,at least one of location, square footage, cabinet type, roof type,siding type, type of fireplace, type of windows, or material type, etc.At a block 1065, the method 1060 may include receiving, e.g., via theone or more processors and/or transceivers (such as via wiredcommunication or data transmission, and/or via wireless communication ordata transmission over one or more radio links or communicationchannels), one or more images, such as digital images acquired via amobile device or smart home controller, of an undamaged insurable asset(such as one or more images submitted by an insured party via a webpage,website, mobile device, and/or smart home controller). Further, at ablock 1068, the method 1080 may include inputting, e.g., via the one ormore processors, the one or more images of the undamaged insurable assetinto a processor having the trained machine learning algorithm installedin a memory unit. The trained machine learning algorithm may, based uponthe one or more images, identify or determine a risk profile for theundamaged insurable asset to facilitate generating an insurance quotefor the undamaged insurable asset.

In one embodiment (not shown in FIG. 14), the method 1060 may includegenerating an insurance policy and/or determining an insurance rate forthe undamaged insurable asset based at least in part upon the riskprofile developed for the undamaged insurable asset. For example, theinsurance rate may include a usage-based insurance (UBI) rate. Theinsurance policy and/or the insurance rate may be electronicallytransmitted to an owner of the undamaged insurable asset for reviewand/or approval, which may be provided by the owner electronically, ifdesired.

Although the present invention has been described in considerable detailwith reference to certain preferred versions thereof, other versions arepossible, which may include additional or fewer features. For example,additional knowledge may be obtained using identical methods. Thelabeling techniques described herein may be used in the identificationof fraudulent claim activity. The techniques may be used in conjunctionwith co-insurance to determine the relative risk of pools of customers.External customer features, such as payment histories, may be taken intoaccount in pricing risk. Therefore, the spirit and scope of the appendedclaims should not be limited to the description of the preferredversions described herein.

Machine Learning & Other Matters

The computer-implemented methods discussed herein may includeadditional, less, or alternate actions, including those discussedelsewhere herein. The methods may be implemented via one or more localor remote processors, transceivers, servers, and/or sensors (such asprocessors, transceivers, servers, and/or sensors mounted on drones,vehicles or mobile devices, or associated with smart infrastructure orremote servers), and/or via computer-executable instructions stored onnon-transitory computer-readable media or medium.

Additionally, the computer systems discussed herein may includeadditional, less, or alternate functionality, including that discussedelsewhere herein. The computer systems discussed herein may include orbe implemented via computer-executable instructions stored onnon-transitory computer-readable media or medium.

A processor or a processing element may be trained using supervised orunsupervised machine learning, and the machine learning program mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data. Forinstance, machine learning may involve identifying and recognizingpatterns in existing text or voice/speech data in order to facilitatemaking predictions for subsequent data. Voice recognition and/or wordrecognition techniques may also be used. Models may be created basedupon example inputs in order to make valid and reliable predictions fornovel inputs.

Additionally or alternatively, the machine learning programs may betrained by inputting sample data sets or certain data into the programs,such as drone, autonomous or semi-autonomous drone, image, mobiledevice, smart or autonomous vehicle, and/or intelligent home, building,and/or real property telematics data. The machine learning programs mayutilize deep learning algorithms that may be primarily focused onpattern recognition, and may be trained after processing multipleexamples. The machine learning programs may include Bayesian programlearning (BPL), voice recognition and synthesis, image or objectrecognition, optical character recognition, and/or natural languageprocessing—either individually or in combination. The machine learningprograms may also include natural language processing, semanticanalysis, automatic reasoning, and/or machine learning.

Supervised and/or unsupervised machine learning techniques may be used.In supervised machine learning, a processing element may be providedwith example inputs and their associated outputs, and may seek todiscover a general rule that maps inputs to outputs, so that whensubsequent novel inputs are provided the processing element may, basedupon the discovered rule, accurately predict the correct output. Inunsupervised machine learning, the processing element may be required tofind its own structure in unlabeled example inputs.

Additional Considerations

With the foregoing, any users (e.g., insurance customers) whose data isbeing collected and/or utilized may first opt-in to a rewards, insurancediscount, or other type of program. After the user provides theiraffirmative consent, data may be collected from the user's device (e.g.,mobile device, smart home controller, smart or autonomous vehicle, orother smart devices). In return, the user may be entitled insurance costsavings, including insurance discounts for auto, homeowners, mobile,renters, personal articles, and/or other types of insurance.

In other embodiments, deployment and use of neural network models at auser device (e.g., the client 502 of FIG. 5) may have the benefit ofremoving any concerns of privacy or anonymity, by removing the need tosend any personal or private data to a remote server (e.g., the server504 of FIG. 5).

The following additional considerations apply to the foregoingdiscussion. Throughout this specification, plural instances mayimplement operations or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. These and othervariations, modifications, additions, and improvements fall within thescope of the subject matter herein.

The patent claims at the end of this patent application are not intendedto be construed under 35 U.S.C. § 112(f) unless traditionalmeans-plus-function language is expressly recited, such as “means for”or “step for” language being explicitly recited in the claim(s). Thesystems and methods described herein are directed to an improvement tocomputer functionality, and improve the functioning of conventionalcomputers.

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment” or “an embodiment”means that a particular element, feature, structure, or characteristicdescribed in connection with the embodiment is included in at least oneembodiment. The appearances of the phrase “in one embodiment” in variousplaces in the specification are not necessarily all referring to thesame embodiment.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent).

In addition, use of the “a” or “an” are employed to describe elementsand components of the embodiments herein. This is done merely forconvenience and to give a general sense of the description. Thisdescription, and the claims that follow, should be read to include oneor at least one and the singular also includes the plural unless it isobvious that it is meant otherwise.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Additionally, certain embodiments are described herein as includinglogic or a number of routines, subroutines, applications, orinstructions. These may constitute either software (e.g., code embodiedon a machine-readable medium) or hardware. In hardware, the routines,etc., are tangible units capable of performing certain operations andmay be configured or arranged in a certain manner. In exampleembodiments, one or more computer systems (e.g., a standalone, client orserver computer system) or one or more hardware modules of a computersystem (e.g., a processor or a group of processors) may be configured bysoftware (e.g., an application or application portion) as a hardwaremodule that operates to perform certain operations as described herein.

In various embodiments, a hardware module may be implementedmechanically or electronically. For example, a hardware module maycomprise dedicated circuitry or logic that is permanently configured(e.g., as a special-purpose processor, such as a field programmable gatearray (FPGA) or an application-specific integrated circuit (ASIC) toperform certain operations. A hardware module may also compriseprogrammable logic or circuitry (e.g., as encompassed within ageneral-purpose processor or other programmable processor) that istemporarily configured by software to perform certain operations. Itwill be appreciated that the decision to implement a hardware modulemechanically, in dedicated and permanently configured circuitry, or intemporarily configured circuitry (e.g., configured by software) may bedriven by cost and time considerations.

Accordingly, the term “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. Considering embodiments inwhich hardware modules are temporarily configured (e.g., programmed),each of the hardware modules need not be configured or instantiated atany one instance in time. For example, where the hardware modulescomprise a general-purpose processor configured using software, thegeneral-purpose processor may be configured as respective differenthardware modules at different times. Software may accordingly configurea processor, for example, to constitute a particular hardware module atone instance of time and to constitute a different hardware module at adifferent instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multipleof such hardware modules exist contemporaneously, communications may beachieved through signal transmission (e.g., over appropriate circuitsand buses) that connect the hardware modules. In embodiments in whichmultiple hardware modules are configured or instantiated at differenttimes, communications between such hardware modules may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware modules have access. Forexample, one hardware module may perform an operation and store theoutput of that operation in a memory product to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory product to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput products, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions. The modulesreferred to herein may, in some example embodiments, compriseprocessor-implemented modules.

Similarly, the methods or routines described herein may be at leastpartially processor-implemented. For example, at least some of theoperations of a method may be performed by one or more processors orprocessor-implemented hardware modules. The performance of certain ofthe operations may be distributed among the one or more processors, notonly residing within a single machine, but deployed across a number ofmachines. In some example embodiments, the processor or processors maybe located in a single location (e.g., within a building environment, anoffice environment or as a server farm), while in other embodiments theprocessors may be distributed across a number of locations.

The performance of certain of the operations may be distributed amongthe one or more processors, not only residing within a single machine,but deployed across a number of machines. In some example embodiments,the one or more processors or processor-implemented modules may belocated in a single geographic location (e.g., within a buildingenvironment, an office environment, or a server farm). In other exampleembodiments, the one or more processors or processor-implemented modulesmay be distributed across a number of geographic locations.

Some embodiments may be described using the expression “coupled” and“connected” along with their derivatives. For example, some embodimentsmay be described using the term “coupled” to indicate that two or moreelements are in direct physical or electrical contact. The term“coupled,” however, may also mean that two or more elements are not indirect contact with each other, but yet still co-operate or interactwith each other. The embodiments are not limited in this context.

Upon reading this disclosure, those of skill in the art will appreciatestill additional alternative structural and functional designs for asystem and a process of automatically obtaining and/or maintaininginsurance coverage through the principles disclosed herein. Thus, whileparticular embodiments and applications have been illustrated anddescribed, it is to be understood that the disclosed embodiments are notlimited to the precise construction and components disclosed herein.Various modifications, changes and variations, which will be apparent tothose skilled in the art, may be made in the arrangement, operation anddetails of the method and apparatus disclosed herein without departingfrom the spirit and scope defined in the appended claims.

What is claimed:
 1. A real property monitoring system, comprising: aplurality of sensors fixedly disposed at respective locations at abuilding, each sensor monitoring a respective dynamic, physicalcharacteristic associated with the building, and at least some of theplurality of sensors being fixedly attached to the building; a datastorage entity communicatively connected to the plurality of sensors andstoring dynamic characteristic data that is indicative of respectivedynamic, physical characteristics detected by the plurality of sensors,the dynamic characteristic data generated based upon signals transmittedby the plurality of sensors; a network interface via which third-partyinput is received by the real property monitoring system, thethird-party input including digitized information that is descriptive ofan event impacting the building; one or more processors; and a damagedetection module comprising a set of computer-executable instructionsstored on one or more memories, wherein the set of computer-executableinstructions, when executed by the one or more processors, cause thesystem to: train, by utilizing the third-party input and the dynamiccharacteristic data corresponding to the building, an analytics modelthat is predictive of one or more conditions associated with thebuilding; apply the trained-analytics model to at least one of thedynamic characteristic data corresponding to the building or additionalcharacteristic data corresponding to the building to thereby discover atleast one of the one or more conditions associated with the building,the at least one of the one or more conditions including particulardamage to the building that is associated with the event; and cause anindication of the particular damage to the building to be transmitted toat least one of a remote computing device or a user interface.
 2. Thereal property monitoring system of claim 1, wherein the dynamiccharacteristic data is time-series data, and the digitized informationdescriptive of the event includes one or more timestamps indicative ofthe occurrence of the event.
 3. The real property monitoring system ofclaim 1, wherein the third-party input includes at least one of digitalnotes or digital images associated with a file of an insurance claim,the at least one of the digital notes or the digital images generated byan insurance provider corresponding to the insurance claim.
 4. The realproperty monitoring system of claim 3, wherein the at least one of thedigital notes or the digital images are generated by at least one of acomputing device or system, an adjuster, a call-center representative,an insurance agent, an imaging system, or a drone of the insuranceprovider.
 5. The real property monitoring system of claim 3, wherein acontent of the digitized information included in the third-party inputis generated by a party that is not associated with the insuranceprovider and is not an end-user of the real property monitoring systemcorresponding to the building.
 6. The real property monitoring system ofclaim 1, wherein the third-party input, the dynamic characteristic datacorresponding to the building, and data indicative of one or more staticcharacteristics of the building are utilized to train the analyticsmodel.
 7. The real property monitoring system of claim 6, wherein theone or more static characteristics of the building include at least oneof: a type of the building; a material or product used in constructionof the building; or a make, model, and/or year of an appliance insidethe building.
 8. The real property monitoring system of claim 1, whereinthe third-party input, the dynamic characteristic data corresponding tothe building, and historical insurance claim data are utilized to trainthe analytics model.
 9. The real property monitoring system of claim 8,wherein the historical insurance claim data includes historicalinsurance claim data corresponding to one or more other buildings. 10.The real property monitoring system of claim 1, wherein the at least oneof the one or more conditions associated with the building furtherincludes a cause of loss corresponding to the particular damage to thebuilding, and an indication of the cause of loss is transmitted to theat least one of the remote computing device or the user interface.
 11. Acomputer-implemented method of detecting damage and other conditions ata building, the method comprising: monitoring, using a plurality ofsensors included in a real property monitoring system, a plurality ofdynamic, physical characteristics associated with the building, theplurality of sensors fixedly disposed at respective locations at thebuilding and at least some of the plurality of sensors being fixedlyattached to the building; storing, in a data storage entity included inthe real property monitoring system, dynamic characteristic data that isindicative of the plurality of dynamic, physical characteristicsassociated with the building and monitored by the plurality of sensors,the dynamic characteristic data generated based upon signals transmittedby the plurality of sensors; obtaining, via a network interface of thereal property monitoring system, input whose content is generated by athird-party, the third-party input including digital data that isdescriptive of an event impacting the building; training, by aninformation processor of the real property monitoring system and byusing the third-party input and the dynamic characteristic data of thebuilding, an analytics model that is predictive of one or moreconditions associated with the building; applying, by the informationprocessor, the trained, analytics model to at least one of the dynamiccharacteristic data corresponding to the building or additionalcharacteristic data corresponding to the building, thereby discoveringat least one of the one or more conditions associated with the building,the at least one of the one or more conditions including particulardamage to the building that is associated with the event; andtransmitting an indication of the particular damage to the building toat least one of a remote computing device or a user interface.
 12. Themethod of claim 11, wherein obtaining the third-party input descriptiveof the event corresponding to the building comprises receiving at leastone of digital notes or digital images associated with a file of aninsurance claim.
 13. The method of claim 12, wherein at least a portionof the at least one of the digital notes or digital images weregenerated by an insurance provider corresponding to the insurance claim.14. The method of claim 13, wherein at least another portion of the atleast one of the digital notes or digital images were generated by aparty other than the insurance provider and other than an end-user ofthe real property monitoring system.
 15. The method of claim 11, whereintraining the analytics model by using the third-party input and thedynamic characteristic data of the building comprises training theanalytics model by using the third-party input, the dynamiccharacteristic data of the building, and data indicative of one or morestatic characteristics of the building.
 16. The method of claim 15,wherein the one or more static characteristics of the building includeat least one of: a type of the building, a material or product used toconstruct the building, or a make, model, and/or year of an applianceinside the building.
 17. The method of claim 11, wherein training theanalytics model by using third-party input and the dynamiccharacteristic data of the building comprises training the analyticsmodel by using the third-party input, the dynamic characteristic data ofthe building, and historical insurance claim data.
 18. The method ofclaim 17, wherein the historical insurance claim data includes at leastone of: data included in respective files of one or more historicalinsurance claims corresponding to the building or data included inrespective files of one or more historical insurance claimscorresponding to another building.
 19. The method of claim 11, whereindiscovering the at least one of the one or more conditions associatedwith both the building and the event further includes discovering acause of loss corresponding to the particular damage associated with thebuilding.
 20. The method of claim 19, wherein discovering the cause ofloss comprises discovering a new cause of loss that is not included in aset of causes of loss utilized by an insurance provider.